The Chechen Wars Essay -- Islam in the North Caucasus 2014

From Western crowds, Chechnyaâ€whether as a self-ruling oblast, a sovereign state, or a war zoneâ€has never got a lot of thought. Only one of many ethnic gatherings inside Russia who have announced since the finish of the Soviet Union their entitlement to self-rule and self-assurance, the Chechens’ battle for autonomy was muffled in the bedlam of calls for freedom during the 1990s. Nonetheless, in a world so significantly influenced by the occasions of September 11, 2001 and given the job of Chechen nonconformist gatherings in bombings of Russian high rises in 1999 (which executed more than 300) and the prisoner taking of a Russian performance center in 2002 (which brought about the passings of 130 Russians and 30 dissidents), the talk of Islamic fundamentalism and the wording of fear based oppression has carried the Chechen individuals to the cutting edge of worldwide concern (Trenin and Malashenko, 2004, p. 45). However the underlying foundations of the contention in C hechnya, which have scorned two wars with the Russian Federation in the course of recent decades, are characterized neither by psychological oppressor exercises or the Islamists who have as of late come to embody the most harmful of the nonconformist dissidents; rather, the source is in the hundreds of years long fashioning of a gathering that has confronted basic abuse from the Russian Empire, the Soviet Union, and the Russian Federation. Ethnicity aggravated with another accentuation on fundamentalist strict philosophy has enormously muddled a battle that has profited the financial and political interests of gatherings as divergent as chose authorities, wrongdoing supervisors, business pioneers, and worldwide governments (Politkovskaya, 2003). War has created the financial and social breakdown of Chechnya and at the same time humiliated a Russia mammoth whose parti... ...thcaucasus.pdf Jaimoukha, A. (2005) The Chechens: A Handbook. New York: Routledge. Meier, A. (2005). Chechnya: To the Heart of a Conflict. New York: W. E. Norton and Organization. Nikolaev. Y. V., Ed. (2013). The Chechen Tragedy: Who is to Blame? Cormack, New York: Nova Science Publishers, Inc. (Walk 19, 2013) Oliker, O. (2001). Russia’s Chechen Wars: 1994-2000. Washington: RAND. Politkovskaya, A. (2003). A Small Corner of Hell: Dispatches from Chechnya. College of Chicago Press Tishkov, V. (2004). Chechnya: Life in a War Torn society. Berkeley, California: The University of California Press. Trenin, D. V. and Malashenko, A. V. (2004). Russia’s Restless Frontier: The Chechnya Factor in Post-Soviet Russia. Washington: Carnegie Endowment for Peace. http://onlinelibrary.wiley.com/doi/10.1002/j.1538-165X.2005.tb01379.x/dynamic

Controversial Images in Art Assignment Example | Topics and Well Written Essays - 500 words

Questionable Images in Art - Assignment Example Something about the cross itself is that Christians treat it like a style adornment. At the point when someone sees it, the person in question is sickened by it. In any case, in the genuine sense, it speaks to the torturous killing of a man. This disputable photograph isn't suggested for open presentations. Being a photograph that shows private parts, in can be seen by youngsters when shown in broad daylight. In this manner, its showcase out in the open spots would degenerate children’s minds. What's more, they can grow up having a poor otherworldly attitude since they have seen a photographic presentation of Christ’s private parts. With numerous assaults made on the photograph in broad daylight puts, this photograph isn't useful for open presentations. Numerous Christians discover the photograph profoundly hostile. For example, Serrano’s work prompted a congressional discussion on United States’ open expressions financing, which was held in France. During the discussion, the photograph was truly assaulted. Likewise, a gathering of Catholics who accumulated outside Edward Tyler Nahem display in midtown Manhattan contradicted this work when the show was opened (Chapman). As indicated by Chapman, specialists convey personality through unique or theoretical methods. It in this way implies it is the obligation of the craftsman to convey through theoretical methods, for example, photos. Notwithstanding, utilizing this methods for correspondence, it ought not trigger contention among the target group. The craftsman needs to abstain from utilizing a photograph that uncovered nakedness since the photograph may lead moral rot among kids who get to it. Furthermore, morals should direct what the craftsman does. He ought to be obliged not to knowing or unconsciously affront Christianity as a religion. It is additionally his express duty not to make unreasonable convictions in different religions. Causing individuals to have faith in different things about their religion is a major peril to the religion they have confidence in (Chapman). The photograph is

How to Find a Good Tutor Online

How to Find a Good Tutor Online Finding a Good Tutor Online That Will Help You Get Ahead Finding a Good Tutor Online That Will Help You Get Ahead Figuring out how to find a good tutor online takes more than just a quick Google search. In fact, a search can bring up a variety of results, making it seem impossible to tell which service will be the best match for you and your needs. You want to be able to find someone you can trust to make sure that they can help you get to where you need to be, so you don’t want just anyone to be your tutor. Here are some of the things you should look for before you sign up for online tutoring sessions. First and Foremost, Know What You Want It will help narrow down your search if you plan out exactly what goals you’re trying to achieve. It’s one thing to say that you need help studying Shakespeare, but it’s another thing to figure out exactly why you can’t quite catch the grasp of those Elizabethan phrases. Is it because you don’t understand iambic pentameter, or is there a deeper issue at play? Once you ask yourself those tough questions you’ll know what kind of tutor you’re looking for. Look at Their Background and Education This might be obvious to you, but a good online tutor should be properly trained and have a good education, particularly in the subject they are helping you with. You want to be sure that the person you’re relying on for academic success can actually assist you in achieving that. All of our writers at Homework Help Global are highly educated and experienced, with a multitude of different degrees from all across the globe. Read Testimonials and Reviews If you read testimonials and reviews it will help you figure out if you’ve found someone who can really help you. Listening to what their previous clients have to say is a great way to get an accurate idea of what your tutor is going to be like once you start sessions with them. However, if your potential tutor doesn’t have any great reviews posted, take caution before you proceed. Your Search for a Great Online Tutor Stops Here Homework Help Global provides online tutoring services in a variety of subject areas, from ESL training to math homework support. We are dedicated to helping you reach your full potential during your time in school, no matter what level of study you’re in, and all of our tutors go above and beyond for their students. Get a quote for online tutoring services now to take that next step toward a better academic future. References: Bahsoun, P. (n.d.) How to find a tutor online. Care.com. Retrieved from . How to Find a Good Tutor Online Finding a Good Tutor Online That Will Help You Get Ahead Finding a Good Tutor Online That Will Help You Get Ahead Figuring out how to find a good tutor online takes more than just a quick Google search. In fact, a search can bring up a variety of results, making it seem impossible to tell which service will be the best match for you and your needs. You want to be able to find someone you can trust to make sure that they can help you get to where you need to be, so you don’t want just anyone to be your tutor. Here are some of the things you should look for before you sign up for online tutoring sessions. First and Foremost, Know What You Want It will help narrow down your search if you plan out exactly what goals you’re trying to achieve. It’s one thing to say that you need help studying Shakespeare, but it’s another thing to figure out exactly why you can’t quite catch the grasp of those Elizabethan phrases. Is it because you don’t understand iambic pentameter, or is there a deeper issue at play? Once you ask yourself those tough questions you’ll know what kind of tutor you’re looking for. Look at Their Background and Education This might be obvious to you, but a good online tutor should be properly trained and have a good education, particularly in the subject they are helping you with. You want to be sure that the person you’re relying on for academic success can actually assist you in achieving that. All of our writers at Homework Help Global are highly educated and experienced, with a multitude of different degrees from all across the globe. Read Testimonials and Reviews If you read testimonials and reviews it will help you figure out if you’ve found someone who can really help you. Listening to what their previous clients have to say is a great way to get an accurate idea of what your tutor is going to be like once you start sessions with them. However, if your potential tutor doesn’t have any great reviews posted, take caution before you proceed. Your Search for a Great Online Tutor Stops Here Homework Help Global provides online tutoring services in a variety of subject areas, from ESL training to math homework support. We are dedicated to helping you reach your full potential during your time in school, no matter what level of study you’re in, and all of our tutors go above and beyond for their students. Get a quote for online tutoring services now to take that next step toward a better academic future. References: Bahsoun, P. (n.d.) How to find a tutor online. Care.com. Retrieved from .

Nazi s Persecution Of The Handicapped Essay - 1404 Words

Nazi’s Persecution of the Handicapped Frank Cai History November 8th Holocaust is considered one of the worst man-caused disaster ever in the history of human life. Hundreds of millions of people died during the Holocaust. Even worse, the victim of the Holocaust is based on race. Why did Adolf Hitler pick on the Jews? Because when he wants to rise power, one of the most common ways is propaganda. He said that Jews are the ones that ruined their country. Many Jews were killed; they were also different kinds of people: just like Jehovah’s Witnesses, Roma Gypsies, Handicapped and many others. While the soldiers were fighting at the front line, hundreds of thousands of disabled were killed. Why were the handicapped killed, what’s wrong with them? During the time of the war, everything was scarce, the food, the ammo, and many other resources. And a bunch of other works that they need to do with the use of human resources. Germany is a small country, with not many workers, and most people that are strong enough were in the army and went to war. They need enough worker to provide them with the enough resources that they need. Any people that are in Germany and are not strong enough (except for children) to work are killed. Hitler and the Nazi think that if they are not strong enough to work for us and using our resources, what’s the point keeping them alive then? On July 14, 1933, the Nazi government instituted the â€Å"Law for the Prevention of Progeny with HereditaryShow MoreRelatedThe Victims Of The Holocaust836 Words   |  4 Pagesminorities, whose death toll equaled 2,285,000, a combined total that clearly showed how determined the Nazis were in destroying their targeted victims in the Holocaust.. The homosexuals were a major group targeted in the Holocaust, and between 5,000 and 15,000 members were targeted (Five Million Forgotten - Holocaust s Non-Jewish Victims). The main reason the homosexuals were targeted was because â€Å"the Nazis believed that male homosexuals were weak, effeminate men who could not fight for the German nationRead MoreThe Effects Of Jews On Jewish Population During The Nazi Regime1119 Words   |  5 Pagesimportant topic is being researched, and it concerns the Final Solution of the Nazis concerning the Jews. On January 20th 1942, 15 leading officials of the Nazi state met at a villa in Wannsee, a suburb of Berlin, to discuss the â€Å"Final solution of the Jewish Question† (â€Å"The Final Solution,† 2015). They used the term â€Å"Final Solution† to refer to their plan to annihilate the Jewish people. It is not known when the leaders of Nazi Germany definitively decided to implement their plan to eradicate the Jews†Read MoreSenderS Profile Photofrank E. Smart. Holocaust Essay.1023 Words   |  5 Pages Sender s profile photo Frank E. Smart Holocaust Essay Mr. Grosse Feb 9 The Holocaust The Holocaust was the state-sponsored persecution also murder 6 Million Jews by the Nazi regimes. holocaust is also a Greek word meaning â€Å"Sacrifice by Fire†. The Nazi came in power in Germany in January 1933. They all believed that Germans was â€Å"Superior† and that the Jews, were also alien threating to call German racial community. In 1933, The Jewish population of Europe they all stood over nine millionRead MoreThe Holocaust And The Nazi War1011 Words   |  5 PagesHolocaust The Holocaust was the state-sponsored persecution also murder 6 Million Jews by the Nazi regimes. holocaust is also a Greek word meaning â€Å"Sacrifice by Fire†. The Nazi came in power in Germany in January 1933. They all believed that Germans was â€Å"Superior† and that the Jews, were also alien threating to call German racial community. In 1933, The Jewish population of Europe they all stood over nine million. The Jews lived in the countries that Nazi Germany would occupy of the influence duringRead MoreExtended Definition of a Modern Hero Essay706 Words   |  3 Pagesexpressions of courage and goodwill. In the early 1970’s, a gay man named Harvey Milk refused to accept discrimination as a homosexual and gained fame as the first openly gay man in the United States to win an election for public office. After running twice, the citizens elected Milk as a San Francisco City Supervisor in 1977. Milk constantly told gay people to remain hopeful and said, â€Å"We have to make up for hundreds of years of persecution† (Gold). Oliver â€Å"Bill† Sipple, a gay man who saved PresidentRead MorePeople Were Persecuted During The Events Of World War II1466 Words   |  6 Pagesheil to the Nazis and their ruler, these groups, including numerous others, were imprisoned in concentration camps and punished for their religions, beliefs, and ways of life. Some fell victim to merciless Nazi persecution, while others were murdered almost instantaneously. Many died as prisoners of harsh concentration camps. Upon entering these camps, captives were stripped of their identity and force d into a life of brutal confinement. Jews and gypsies were the main targets of Nazi oppression,Read MoreThe Holocaust Denial1324 Words   |  6 Pagesand ruled over Germany. This fascist leader founded his own Nazi party and was determined to establish a nation with pure German or an Aryan race. In order to achieve his goal, Hitler commanded isolation and eventually extermination of the impure groups in Germany. Anyone with impure blood is Germans enemy. Under Hitlers regime, Germans considered the following groups as their enemies: Jews, Gypsies, homosexuals, and mentally handicapped people (Thirty-six questions). Germans targeted Jews the mostRead MoreThe Holocaust Was An Ultimate Abomination Of Nazi1484 Words   |  6 PagesThe Holocaust was an ultimate abomination of Nazi racism that occurred between 1938 and 1945. The word Holocaust derived from the Greek word holokauston, which stands for a burnt sacrifice that is offered whole to God. The word was chosen for this occurrence because of the amount of dead bodies that were cremated in open fires by Nazis. The Holocaust was known for the mass murders of European Jews that took place during the Second World War. European Jews were the fundamental victims during the HolocaustRead MoreEssay on Human Rights Violations857 Words   |  4 Pagesrace or religion different from his or her own. In the early twentieth century itself, we faced atrocities such as the Armenian Massacre, the rape of Nanking and many more. One such crime against the human race that can overthrow all of them is, the Nazi Holocaust led by Adolf Hitler. After World War I, Germany was in a condition of total chaos. The Weimar Republic that was set up by the League of Nations was not holding much water and the citizens of Germany were looking for some authority to putRead MoreGerman Nazi: The Wannsee Protocol Essay1439 Words   |  6 Pagesranking officials in the Nazi regime. The conference was set up in order to discuss and implement ‘The Final Solution to the Jewish Question’ in regards to the Jewish population in Europe. The minutes of this conference were written down and are now known as the Wannsee Protocol. Even before the Wannsee Conference took place Jews were already being executed by the Einsatzgruppen, or otherwise known as the mobile killing units of the SS. Anti- Semitism and the Persecution of the Jewish population

TRADEMARK PROTECTION AN INTERNATIONAL PERSPECTIVE - Free Essay Example

Sample details Pages: 8 Words: 2481 Downloads: 1 Date added: 2017/06/26 Category Law Essay Type Review Level High school Did you like this example? ABSTRACT Trademarks are signs and combinations that identify goods and services of a particular individual offered in a market. Today the trademark is a way to attract the public. Consumers look at trademarks to choose goods and services, which increases the role of trademarks in global marketing. Don’t waste time! Our writers will create an original "TRADEMARK PROTECTION: AN INTERNATIONAL PERSPECTIVE" essay for you Create order Trademarks are important in the sense that most of the consumers rely on the symbols, letters, or labels that the company attached with its products in order to buy them. Often, consumers are deceived by selling local quality products under the brand name. This not only break the trust of the consumers but it also hamper the reputation and goodwill of the brand name and its business. Hence, trademark needs to be protected from such fraudulent activities not only nationally but internationally too. Sometimes, trademark is infringed in a foreign country and due to territorial restrictions; the trademark owner is not able to protect his mark in that country. Our intellectual property system offers a legal means for such protection. There exists a complete international system for trademark protection. Several international agreements have been signed to facilitate the international protection of intellectual property rights. The oldest is the Paris Convention of 1883 and the most recent is TRIPS in 1994. There are several other global and regional agreements, signed between the Paris Convention and TRIPS, which are still in force today such as the 1891 Madrid Agreement on the International Registration of Trademarks, the 1989 Madrid Protocol on the International Registration of Trademarks, and the 1994 Trademark Law Treaty. This article examines various treaties, convention and agreements made internationally for the protection of trademark in the global market. INTERNATIONAL AGREEMENTS à ¢Ã¢â€š ¬Ã¢â‚¬Å" The Paris Convention and TRIPS both provides many general principles and rules for the protection of intellectual property rights. PARIS CONVENTION The Paris Convention for the Protection of Intellectual Property is one of the oldest and important treaties for the protection of intellectual property rights signed in 1883 in Paris. It also established a union named Paris Union for protecting intellectual property rights. It applies to all intellectual properties such as trademarks, utility models, patents, geographical indications. The Paris Convention provides three principles for protection of intellectual properties[1]: National Treatment à ¢Ã¢â€š ¬Ã¢â‚¬Å" The Paris Convention provides that each member country of the convention must provide equal and same protection of intellectual property which it grants to its own citizens, to the nationals of other member countries. For example, if a citizen of India wishes to obtain a Trademark Protection in United States, he will get same protection and rights under the same conditions which United States will provide to its own nationals as both India and United States are the signatories of Paris Convention. Also, the citizens of non-member countries are also entitled to national treatment under the Convention but with some limitations. This principle also applies to all TRIPS member states. Right of Priority à ¢Ã¢â€š ¬Ã¢â‚¬Å" It means that the applicant who is already protected in one of the member states can apply within a certain period of time for the protection in other member states. The subsequent applications filed will be treated as if they had been filed on the same day as the first application. In simple words, they will have priority over the applications filed by others during that period for the same invention. The advantage of this provision is that the applicants have the option to file the application later in the countries in which they wish to protect their mark and are not required to present all of their applications at the same time. Common Rules à ¢Ã¢â€š ¬Ã¢â‚¬Å" All the signatories of the convention are governed by their own domestic law for registration of intellectual property rights. Therefore, the annulment or nullification of the registration of a mark in one Member State will not affect the validity of the registration in other Member States. This means that the trademark owner is subject exclusively to the national laws of each country. But often, some national laws prohibit registration of numbers or letters, whereas others allow such trademarks. In that case it becomes very difficult for the trademark owner to use a mark in the same form in all the countries. But, the Paris Convention provides that the trademark that has been registered in its country of origin in compliance with domestic law is to be registered in other member states as it is. TRIPS (The Agreement on Trade-Related Aspects of Intellectual Property Rights) TRIPS is an international agreement administered by the World Trade Organization (WTO) which provides minimum standards for regulation of intellectual property rights. It was negotiated in 1994. It covers almost all intellectual property rights such as copyright, trademarks including service marks, geographical indications, industrial designs, patents including the protection of new varieties of plants, the layout-designs of integrated circuits and trade secrets.[2] It incorporates some of the provisions of Paris Convention (1967) also which includes national treatment principle. Article 3 of TRIPS provides for reciprocity between member states. It restricts the discrimination between a Member countries own nationals and the nationals of other Member countries. It means that each member state must grant the citizens of other member states the same intellectual property rights protection which it grants to its own citizens. TRIPS have also introduced the most favoured nation prin ciple which forbids the discrimination between nationals of other member countries. Article 4 of TRIPS provides that all advantages, favours, privileges or immunities granted by a member to its own citizens will be extended to all other members in the same way and without any further conditions. However, the national treatment and most favoured nation principle do not apply to agreements such as Madrid Agreement and Madrid Protocol which are mainly introduced for the international or regional registration of intellectual property rights.[3] It is mandatory for all member states of TRIPS to introduce procedures into their national legislation for the actions to be taken against any infringement of intellectual property rights. Any victim can go to any judicial or administrative authority for seeking remedies in respect of the infringement. Remedies can be in the form of injunction, seizure or compensation for the loss of reputation or goodwill. Articles 15-21 of TRIPS lay down the rules for protection of trademarks. Article 15(1) provides that all signs and combinations of signs that are capable of distinguishing the goods and services of one undertaking from another are capable of acquiring trademark protection. Distinctiveness is thus the sole condition for protection of a trademark. If a mark is not able to distinguish between the goods of two persons, it will not be allowed to be registered. Article 15(4) extends the protection to service marks also so that the nature of a product or a service may not be an obstacle to registration of the mark. Article 15(5) of TRIPS only provides for the obligation to publish the trademark either before or immediately after registration and to allow suitable opportunity for an opposing party to apply for cancellation of the registration. Under Article 16, TRIPS recognizes the exclusive right of the trademark holder. During the term of protection, the owner of a trademark enjoys the exclusive right to prevent th ird parties from using either his own mark or a similar mark for same or similar goods or services in the course of trade where such a use would result in a likelihood of confusion among the consumers. TRIPS also extends the protection to well known marks. Thus, Pepsi Company has the right to forbid a shoe manufacturer from using the sign à ¢Ã¢â€š ¬Ã…“Pepsià ¢Ã¢â€š ¬Ã‚  to designate its shoes if consumers would be likely to believe that the shoes were manufactured or endorsed by the Pepsi Company, thereby diluting the à ¢Ã¢â€š ¬Ã…“Pepsià ¢Ã¢â€š ¬Ã‚  trademark. SPECIAL AGREEMENTS à ¢Ã¢â€š ¬Ã¢â‚¬Å" Article 19 of the Paris Convention permits the countries of the Union the right to make separately between themselves special agreements for the protection of intellectual property. Presently, there are four such special agreements existing relating to trademarks: the Madrid Agreement, the Trademark Registration Treaty, the Madrid Protocol, and the Trademark Law Treaty. The Madrid Agreement, Trademark Registration Treaty, and the Madrid Protocol are completely different from TRIPS as these provides for international registration of trademarks but TRIPS does not deal with registration of intellectual property rights. The Madrid Agreement: The Madrid System for the International Registration of Marks is governed by two treaties: The Madrid Agreement and the Madrid Protocol. The Madrid Agreement was incorporated in 1891. It is administered by the International Bureau of the World Intellectual Property Organization (WIPO). Most of the countries have ratified the Madrid Agreement including India, but with the exception of the United States, Japan, the United Kingdom, Ireland, and the Scandinavian countries. The Madrid Agreement provides simple international registration procedures for acquiring trademark protection by providing single international application upon payment of a single fee. The procedure for registration under the Madrid Agreement may be summarized as follows[4]: a citizen of a member state owns a registered trademark in its own country on the basis of this initial registration, the national trademark owner applies for international trademark registration with the International Bureau of the WIPO; in the international application, the applicant lists the member states in which protection is sought; the WIPO distributes the international application to each of the listed states; in each of these states, the international application is treated as a national application Under the Madrid Agreement, if the trademark registered in the country of origin, on which the international registration is based, is nullified, then all the trademarks issued from the international registration also becomes void within five years from the date of international registration. Madrid Agreement has been criticized by many countries on this point.[5] Trademark Registration Treaty The WIPO created the Trademark Registration Treaty in 1973. The United States and thirteen other countries were signatories to it. But till now, many countries including United States have not ratified it. It is now signed only by the Soviet Union and four African countries. This treaty was made with an objective to establish an international trademark  ¬Ãƒâ€šÃ‚ ling system through which citizens residing in one of the member States can easily register trademarks in all other member states just by filing one single application and securing single international registration. The main advantage of this treaty is the simplified procedure to get the trademark registration secured internationally. But it is still not in force.[6] The Madrid Protocol The Madrid Protocol was signed on June 27, 1989, and entered into force on April 1, 1996. There are total 86 countries who are signatories to this protocol including India. It provides a cost-effective and a very efficient way for trademark holders to ensure protection in multiple countries through the filing of one single application with a single office and single fee in one language. Once any member country grants the trademark protection to any applicant, his trademark will be protected in that country as if that country has registered it. Similar to Madrid agreement, here the international application is treated as a national one.[7] The procedure for filing the application under Madrid protocol is that the applicant files an International Application from the national office of his country which will then pass the International Application to WIPO. He can then list those countries in his application in which he sought protection.[8] The duration of protection following an international registration is ten years, renewable under payment of a fee to the International Bureau of the WIPO. The Protocol is a new treaty independent from the Madrid Agreement and introduces new procedures for international registration which entered into force on April 1, 1996. For example, if the applicant selects the European Community Office, the office of harmonization in the internal market (OHIP), the application is treated as an EC trademark application. The Trademark Law Treaty The Trademark Law Treaty was adopted in Geneva on October 27, 1994, and entered into force August 1, 1996. It does not deal with the registration of trademarks but simplify national and regional trademark registration procedures. There are 49 countries which are signatories to it. It also eliminates the formal requirements that are considered to be unnecessary obstacles in the registration process. The treaty applies to trademarks for such as word marks, design marks, mixed marks and three-dimensional marks. The treaty does not apply to sound marks, olfactory marks, collective marks, certification marks or guarantee marks. The provisions of the treaty contain three phases of the registration procedure: (i) the application for registration; (ii) changes after registration; and (iii) renewal. The provisions of the Trademark Law Treaty are not incorporated into TRIPS. The duration of renewal of the registration under this treaty is 10 years.[9] CONCLUSION What conclusions may be drawn from this brief overview of the international trademark protection system? Clearly, the Paris Convention has stood the test of time. Its principles are now incorporated into TRIPS, defining the basic rules of protection of intellectual property rights in the international trade. The recognition and protection of intellectual property rights is one of the conditions for international peace. Apart from various international agreements like Paris Convention and TRIPS, there are various special agreements such as Madrid Agreement and Madrid protocol, trademark law treaty and trademark registration treaty for the protection of trademarks internationally. Madrid Agreement and Madrid protocol which are part of the Madrid system only deals with registration aspect of trademarks whereas all other treaties and convention deals with principles and rules for protecting trademarks and simplifying the trademark registration procedures at the international level. All these treaties and agre ements are incorporated with sole objective to simplify the international procedures for protecting the trademark and to make them cost effective and more efficient so that any person can make his mark registered and enjoy the trademark protection not only in his home country but also internationally. 1 [1] à ¢Ã¢â€š ¬Ã…“Summary of the Paris Convention for the Protection of Industrial Propertyà ¢Ã¢â€š ¬Ã‚  (1883), available at lt;https://www.wipo.int/treaties/en/ip/paris/summary_paris.htmlgt; (accessed on 15th Nov, 2014) [2] Wolf R. MEIER-EWERT, à ¢Ã¢â€š ¬Ã…“A Business-oriented overview of Intellectual Property for Law Studentsà ¢Ã¢â€š ¬Ã‚  available at lt;https://www.wipo.int/edocs/mdocs/sme/en/wipo_smes_ge_2_06/wipo_smes_ge_2_06_www_63216.pptgt; (accessed on 15th Nov, 2014) [3] Supra note 1 [4] à ¢Ã¢â€š ¬Ã…“Guide To The International Registration Of Marks Under The Madrid Agreement And The Madrid Protocolà ¢Ã¢â€š ¬Ã‚  available at lt;https://www.wipo.int/export/sites/www/madrid/en/guide/pdf/guide.pdfgt; (accessed on 15th Nov, 2014) [5] Supra note 4 [6] DONALD W. BANNER, à ¢Ã¢â€š ¬Ã…“Trademark Registration Treatyà ¢Ã¢â€š ¬Ã‚  available at lt;https://ipmall.info/hosted_resources/lipa/trademarks/PreLanhamAct_107_Trademark_Treaty.htmgt; (accessed on 15th Nov, 2014) [7] à ¢Ã¢â€š ¬Ã…“Madrid Protocolà ¢Ã¢â€š ¬Ã‚  available at lt;https://www.uspto.gov/trademarks/law/madrid/gt; (accessed on 15th Nov, 2014) [8] à ¢Ã¢â€š ¬Ã…“Madrid Protocol and Madrid Agreementà ¢Ã¢â€š ¬Ã‚  available at lt;https://www.elkfife.com/madrid-protocol-and-madrid-agreementgt; (accessed on 15th Nov, 2014) [9] à ¢Ã¢â€š ¬Ã…“Summary of the Trademark Law Treaty (TLT)à ¢Ã¢â€š ¬Ã‚  (1994) available at lt;https://www.wipo.int/treaties/en/ip/tlt/summary_tlt.htmlgt; (accessed on 16th Nov, 2014)

Beyond Gdp Paper Free Essays

Special attention is devoted to recent developments in the analysis of sustainability, in the study of happiness, in the theory of social choice and fair allocation, and in the capability approach. It is suggested in the conclusion that, although convergence toward a consensual approach is not impossible, for the moment not one but three alternatives to GDP are worth developing. ( JEL I31, E23, E01) 1. We will write a custom essay sample on Beyond Gdp Paper or any similar topic only for you Order Now Introduction G DP is recurrently criticized for being a poor indicator of social welfare and, therefore, leading governments astray in their assessment of economic policies. As is well known, GDP statistics measure current economic aactivity but ignore wealth variation, international income flows, household production of services, destruction of the natural environment, and many determinants of well-being such as the quality of social relations, economic security and personal safety, health, and longevity. Even worse, GDP increases when convivial reciprocity is replaced by anonymous market relations and when rising crime, pollution, catastrophes, or health hazards trigger * Fleurbaey: CNRS, University Paris Descartes, CORE (Universite de Louvain) and IDEP. Comments, suggestions and advice by S. Alkire, G. Asheim, A. Atkinson, A. Deaton, E. Diewert, R. Guesnerie, D. Kahneman, A. Krueger, I. Robeyns, P. Schreyer, three referees and Roger Gordon (the Editor) are gratefully acknowledged. defensive or repair expenditures. Not surprisingly, the construction of better indicators of social welfare is also, recurrently, a hot issue in public debate and a concern for politicians and governments. The last two decades have witnessed an explosion in the number of alternative indicators and a surge of initiatives from important institutions such as the OECD, the UNDP, the European Union—more recently the French government has appointed a committee, chaired by Joseph E. Stiglitz and including four other Nobel Prize winners, to propose new indicators of â€Å"economic performance and social progress. In the meantime, welfare economics1 has burgeoned in various directions, involving the theory of social choice, the theory of 1 The expression â€Å"welfare economics† is used here in a very broad sense, including all branches of economics that bear on the definition of criteria for the evaluation of social states and public policies. It is not restricted to the narrow confines of Old and New (or New New) Welfare Economics. 1029 1030 Journal of Economic Literature, Vol. XLVII (December 2009) is much less supported by economic theory than is commonly assumed. The extension of this approach to intertemporal welfare as attempted in â€Å"green† accounting adds even more complications. In view of recent developments in the theory of social choice and fairness, it will be argued that the idea of a â€Å"corrected GDP† is still defendable but implies different accounting methods than usually thought. Second, there is the idea of â€Å"Gross National Happiness,† which has been revived by the burgeoning happiness studies. It will be argued here that the happiness revolution might, instead of bringing about the return of â€Å"utility,† ultimately condemn this concept for being simplistic, and reveal that subjective well-being cannot serve as a metric for social evaluation without serious precautions. Third, there is the â€Å"capability approach † proposed by Amartya Sen, primarily as a framework for thinking rather than a precise method of measurement. This approach has now inspired a vvariety of applications, but most of its premoters are reluctant to seek a synthetic index, a famous exception being the Human Development Index (HDI). It will be argued here that a key aspect of this problem is whether individual valuations of the relevant dimensions of capability can and should be taken into account— an issue over which a dialogue with the two previous approaches might prove very useful. Fourth, there is the growing number of â€Å"synthetic indicators† that, following the lead of the HDI, are constructed as weighted averages of summary measures of social performance in various domains. It will be argued here that, if the three other approaches were fully exploited, there would be little reason to keep this fourth approach alive because it is ill-equipped to take account of the distribution of well-being and advantage among the members of society. The paper is structured as follows. Sections 2–4 deal with monetary measures that are linked to the project of a corrected fair allocation, the capability approach, the study of happiness and its determinants, in conjunction with new developments in the philosophy of social justice and the psychology of well-being. These conceptual developments provide new analytical tools that may be directly useful for concrete measurements. About a decade ago, Daniel T. Slesnick (1998) made the following observation: â€Å"While centrally important to many problems of economic analysis, confusion persists concerning the relationship between commonly used welfare indicators and well-established theoretical formulations† (p. 2108). It is probably safe to say that much the same now holds about the relationship between concrete measures of welfare—old, new, and potential—and upto-date theories. It appears timely to ask what the existing academic literature has to say about alternatives to GDP. The practical importance of a measure of social welfare can hardly be overstated. Ppolicy decisions, cost–benefit analyses, international comparisons, measures of growth, and inequality studies constantly refer to evaluations of individual and collective wellbeing. The fact that monetary measures still predominate in all such contexts is usually interpreted as imposed by the lack of a better index rather than reflecting a positive consensus. The purpose of this paper is, in the light of state-of-the-art welfare economics, to examine the pros and cons of the main alternative approaches to the measurement of social welfare from the perspective of ppolicy evaluation as well as international and intertemporal comparisons. Four approaches are discussed here. First, there is the idea of a â€Å"corrected GDP † that would take account, in particular, of nonmarket aspects of well-being and of sustainability concerns. As will be explained here, a basic problem for this approach is that its starting point, national income, as a candidate for a measure of social welfare, Fleurbaey: Beyond GDP: The Quest for a Measure of Social Welfare GDP. Section 2 revisits the classical results involving the value of total consumption and usually invoked in justification of GDP-like measures. This appears important because some of these results are often exaggerated, while others are little known or even susceptible of developments in future research. Section 3 is devoted to the intertemporal extension of this approach, as featured in the Net National Product (NNP) and â€Å"green† accounting. Section 4 turns to measures based on willingness-to-pay and moneymetric utilities, highlighting the connection with recent developments in the theory of social choice and fairness. This section also briefly discusses cost–benefit analysis, which is an important tool for ppolicy evaluation. Sections 5–7 are devoted to the nonmonetary approaches, namely, synthetic indicators such as the HDI (section 5), happiness studies and the various possible indexes of subjective well-being (section 6), and the capability approach (section 7). Section 8 makes concluding remarks about the relative strengths and weaknesses of the various approaches analyzed in the paper and the prospects for future developments and applications. 2. Monetary Aggregates Revisited The project of correcting GDP has been often understood, after William D. Nordhaus and James Tobin’s (1973) seminal work, as adding or subtracting terms that have the same structure as GDP, i. e. , monetary aggregates computed as quantities valued at market prices or at imputed prices in case market prices are not available. As we will see in this section, economic theory is much less supportive of this approach than usually 2 Nordhaus and Tobin (1973) set out to compute â€Å"a comprehensive measure of the annual real consumption of households. Consumption is intended to include all goods and services, marketed or not, valued at market prices or at their equivalent in oopportunity costs to consumers† (p. 24). 1031 thought by most users of national accou nts. Many official reports swiftly gloss over the fact that economic theory has established total income as a good index of social welfare under some assumptions (which are usually left unspecified). To be sure, there is a venerable tradition of economic theory that seeks to relate social welfare to the value of total income or total consumption. 3 Most of that theory, however, deals with the limited issue of determining the sign of the welfare change rather than its magnitude, not to mention the level of welfare itself. In this perspective, the widespread use of GDP per capita, corrected or uncorrected, as a cardinal measure allowing ppercentage scaling of differences and variations appears problematic. 4 In this section, I review the old and recent arguments for and against monetary aggregates as social welfare indicators. . 1 A Revealed Preference Argument Start from the revealed preference argument that, assuming local nonsatiation, if a consumer chooses a commodity bundle x (with ? different commodities) in a budget set defined by the price vector p, then x is revealed preferred to all bundles y such that py px. If x is interior and assuming differentiability, for an infini tesimal change dx, x + dx is strictly preferred to x by the consumer if and only if pdx 0. Note the importance of the interiority assumption here. How to cite Beyond Gdp Paper, Papers

Rubi Transportation Company

Question: Discuss about the Rubi Transportation Company. Answer: Introduction Transportation has been an important policy issue for those with disabilities. The persons with disabilities have been described how the transportation barriers influences their lives (Bell, Stephen and Alex 63). It has been long recognized that individuals with disabilities face many difficulties especially in traveling and this has been associated with the limited life opportunities. These difficulties are usually seen as the barriers to access of the transport system. Individuals needs to feel they are all accepted in the society (Drew, Joseph, Michael and Brian Dollery 634). Rubi Transportation Company has been providing the transport services for sometimes now in Sydney, Australia. The company has been aiming at expanding their market and they are targeting the availability of the transportation services to the disabled individuals (Bryman, Alan 98). Nonetheless, the question, which has risen, is whether the business has the possibility that the market has to offer and how many individuals are expected to avail in this new serve in case it is offered. The research elaborate the case of Rubi Transportation Company on how they are expanding their services to the disabled individuals especially in Australia (King, Nigel, Cassell and Symon 17). The research will examine the business draft profit expenses, quantitative interview questions answer that a client could ask to the business owner at Rubi Transportation company and also how the disable person could pay in accordance to the new South Wales government policies its halfy from the government as a voucher. Additionally, it would be important to look at the literature review of some of the authors that has done research on the subject. Literature review There has been extensive research that have been done on the issue of transportation for the disabled individuals, and the challenges that has affected them. Some of the findings was that the use of the door-to-door services and the use of taxis was most common especially in the Australia (Drummond, Robyn, and Richard Wartho 405). The findings highlighted that the use of taxi was the most preferred means by the disabled individuals as comparison to the public transport since they did not have an access to a vehicle (Polonsky, Michael Jay, and David 45). Other findings showed that the disabled individuals are twice more likely than the non-disabled individuals of turning a job down due to the issues of transportation serviced. These people do not often get out of their homes (Bryman, Alan 97). According, to the research it highlighted further that the disabled people faced a lot of problem especially in their professional as well as personal lives just because of the problems of trans portation (Hung-Wen, Lee 403). If these individuals had an access to a good transportation system, their lives would definitely change for the best. Some of this concern could be door to door dialing a ride service rather than having taxis (Bryman, Alan 113). The drivers should be helpful to help these people when they want to move from one place to place. These issues should be incorporated in the business plan since it is an important part to the business of Rubi transportation services (Drew, Joseph, Michael and Brian Dollery 634). This research is on the Rubi transportation company on quantitative interview questions that a client could ask the business owner. Additionally, it will incorporate profits expenses and business draft that shows how the disable people could pay according to the New South Wales government policies. The government have been in support of the disability plan. This has been part of the New South Wales strategy in order to ensure that the ends of the clien ts are placed at the Centre of the planning and decision-making for the transport system (Saxena 499). Rubi Transportation Company should aim at delivering of high quality services to all the clients including those with the disability (White, Edward 69). The needs of the disabled individuals varies from one person to another, therefore the method of transport that they usually uses on a daily basis varies from one individual to another. It is important to note that the transport operators as well as other providers have found it to be difficult to implement the transport standards (Drew, Joseph, Michael and Brian Dollery 632). There has been several gaps in the data as well as support processes for the transport standards especially on the disabled individuals. Individuals with disability problem have reported inconsistency of implementation of the transport standards in Australia. Concession fares vouchers for people with disabilities If you are individual who has disability in Au stralia there are concession cards vouchers that are designed by the government in order to help you to negotiate the New South Wales policies for public transport. The disabled person can use Welsh concessionary passes at any time of the day. Additionally, the person could request one compassion voucher pass for when they are required to have an assistance to travel (White, Edward 67). However, the cardholder should pay their normal fare that could be full fair, free travel, depending on the concession status. The scheme for applying for the government pass vouchers are run by the local authorities. One should provide evidence to the local government in order to confirm that they are eligible and then they are issued (Gething, Lindsay 502). The government further provides financial assistance, which is needed, for the disabled people to be independent in their local communities (Bell, Stephen, and Alex 65). The taxi transport subsidy scheme provides at least 50% of the taxi fare up which is up to 30 dollars for the individuals with a disability who cannot have an access to the public transport. The NSW government announced it would increase the cap to sixty percent and boost the incentives to put more wheelchair to accessible taxis on the road (Wilson, Jonathan 56). The increase is especially to the individuals for the regional NSW that often have little or perhaps no access to the public transport and can pay large taxi fares since they travel for long distances. The increase of the halfy subsidiary was part of the government response to the point-to-point transport taskforce response on the taxis and ridesharing in NSW. Profit expense draft Rubi transport Company Operation revenue ($)Service sales 50000Total operating revenue 50000 Operating expenses Cost of goods sold 6000Gross profit 44000 Overhead Rent 1500Insurance 300Utilities 500 Total overhead 2300 Operating income 41700Other income expenses Loans interest 5000Earnings before income taxes 36700Income taxes 4000Net earnings 32700 Business draft Executive summaryGeneral company description Products and the services of the companyMarketing planOperational planManagement and the organizationFinancial plan Quantitative interview questions 1. How did you get your idea or concept for the business to expand to the new market? a) Research data on the current market b) Wanting to expand the market base c) Market gap that has existed in the area d) All the above 2. What was your mission at the onset of starting this company? a) For purpose of profit b) To make a difference in the world c) To become the best choice of delivering transport service d) None of the above 3. Which reasons Rubi Transportation Company to expand its services to the disabled? a) To serve the community better b) To give something to the less privileged individuals in the society c) For the purpose of profit d) All the above 4. Which market gap will the company need to focus at the time? a) Communication issuesb) Customer gapc) Policy implementation aspectsd) None of the above 5. Which is the major legal procedures that Rubi Transportation Company has faced when they ventured to the new market? a) Legal release and consenting process b) Privacy issues c) Licenses and permits for implementing the new business ventured) All the above 6. What is the current problem the company is facing? a) Business culture problems b) Human resource issues c) Communication d) All the above 7. How do you intend to advertise on your business? a) Websiteb) Partnershipc) Events and seminarsd) Social media 8. What do you look for in an employee? a) We look for people who are self-motivated and who do not need to be managed b) We need employees who we call triple threats.c) We look for individuals who work and communicate with their peers.d) All the above 9. What made you to choose the current business venture? a) Family and cost of living b) The market is favorable c) The need to help others d) The money was good 10. What is unique about your business? a) The business has narrowed its operation to single target market b) The business provide personalized services to the client c) Delivery of service of the business is effective as well as innovative at the same time d) None of the above References Bell, Stephen, and Alex Park. "The problematic metagovernance of networks: water reform in New South Wales." Journal of Public Policy 26.01 (2006): 63-83.Bryman, Alan. "Integrating quantitative and qualitative research: how is it done?." Qualitative research 6.1 (2006): 97-113.Drew, Joseph, Michael A. Kortt, and Brian Dollery. "Economies of scale and local government expenditure: evidence from Australia." Administration Society 46.6 (2014): 632-653.Drummond, Robyn, and Richard Wartho. "Rims: The Research Impact Measurement Service At The University Of New South Wales." Australian Academic Research Libraries 47.4 (2016): 270-281.Gething, Lindsay. "Sources of double disadvantage for people with disabilities living in remote and rural areas of New South Wales, Australia." Disability Society 12.4 (1997): 513-531.Hung-Wen, Lee. "Factors that influence expatriate failure: An interview study." International Journal of Management 24.3 (2007): 403.King, Nigel, C. Cassell, and G. Symon. "Qu alitative methods in organizational research: A practical guide." The Qualitative Research Interview 17 (1994).Polonsky, Michael Jay, and David S. Waller. Designing and managing a research project: A business student's guide. Sage Publications, 2014.Saxena, K. B. C. "Towards excellence in e-governance." International Journal of Public Sector Management 18.6 (2005): 498-513.White, Edward. "Claims to the benefits of clinical supervision: A critique of the policy development process and outcomes in New South Wales, Australia." International Journal of Mental Health Nursing 26.1 (2017): 65-76.Wilson, Jonathan. Essentials of business research: A guide to doing your research project. Sage, 2014.

Police Department and Organization Ppt free essay sample

Most local law enforcement agencies are small in size and employ many civilians for data processing, finger printing and other clerical duties. Local law enforcement agencies are responsible for patrolling an area or jurisdiction, the apprehension, detention of adult and juvenile criminal suspects, for providing emergency services, community service and relations, criminal and forensic investigations, and enforcing traffic laws. Most local law enforcement agencies also â€Å"perform a standard set of functions and tasks and provide similar services to the community: these include the following: traffic enforcement, narcotics and vice control, accident investigations, radio communications, patrol, peace keeping, crime prevention, property and violent crime investigations, finger printing processing, death investigations, and search and rescue ( Siegel, Senna, 2008). Many local law enforcement agencies have become very involved with schools and the citizens of the community. Many schools have officers on campus to assist teachers and students. Local police also have programs like D. A. R. E. that help educate children and parents about the signs of drug use, the dangers of drug use and domestic violence issues. The rural and outlaying county areas of a city are under the care of the Sheriff’s Department. The Sheriff provides law enforcement to residents living in the county area. Just like local or city police a Sheriff’s Department can vary in size. The Sheriff’s Department’s are assigned their duties by State law and have the primary responsibility for investigating violent crimes in their jurisdiction (Gaines, Miller, 2006). Local police and a Sheriff perform basically many of the same tasks however there are differences between the two agencies. For instance the Sheriff’s Department officers participate in the daily operations of jails. Sheriff Officers are also called upon for search and rescue operations, and Sheriff’s are more involved with the courts than local police officers (Gaines, Miller, 2006). Also local law enforcement agencies perform more traffic related tasks than the Sheriff’s Department. Another important department under the operation of the Sheriff’s Department is the County Coroner’s Office. A County Coroner’s duties vary from county to county but their main function overall is to investigate all unexplained, unnatural, or suspicious deaths (Gaines, Miller, 2006). Coroner’s assist law enforcement agencies with homicide investigations to help determine the accurate cause of death and when and how and an individual was murdered. Here’s another interesting fact; if a Sheriff is arrested or forced to leave his or her position then the County Coroner becomes the main law enforcement officer for that county. The State Police are our most visible for of law enforcement on our highways today. Originally state law enforcement agencies were created to â€Å"assist local police agencies that did not have adequate resources available for crime solving, forensics, and arrest, to investigate criminal activities that have crossed state lines, to provide law enforcement to county and rural areas and to control labor and strike movements (Gaines, Miller, 2006). Today State law enforcement agencies focus on enforcing traffic laws, regulating traffic, investigating motor vehicle accidents and investigating violent crimes. State law enforcement agencies have a wide variety of functions and responsibilities. Basically State police provide the same types of services as local law enforcement agencies except that the State Police may use his or her power throughout the state they work in, whereas local police officers are limited to their use of power within the jurisdiction he or she is working in. In some cases the type of offense committed may determine who has power or jurisdiction over the case. Federal law enforcement is divided into three categories: The Department of Justice, Department of Homeland Security, and the Department of the Treasury. Each of these federal law enforcement agencies works together to solve specific types and forms of crimes. Federal law enforcement agencies are authorized by Congress to enforce specific laws or attend to specific situations (Gaines, Miller, 2006). Under the Department of Justice the following are the following departments: The Federal Bureau of Investigations (FBI), United States Marshal Service (USMS), Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF), and the Drug Enforcement Administration (DEA). The FBI is responsible for investigating federal law violations and has jurisdiction over two hundred federal crimes like sabotage, espionage, kidnapping, bank robberies, extortion, interstate crimes and civil rights violations. The FBI also assists and provides training to other law enforcement agencies. There are eight separate divisions under the control of the FBI and they are the National Security Division, Criminal Investigation Division, the FBI laboratory, Criminal Justice Information Services Division, Information Resources Division, Training Division, Administrative Services Division, and the Critical Incident Response Group (Siegel, Senna, 2006). All eight of these agencies work together to combat worldwide criminal activity such as terrorism, organized crime, foreign intelligence, federal drug offenses and white collar crimes. Under the Department of Homeland security (DHS) the primary function is to protect United States citizens against international and domestic terrorism. There are fifteen separate agencies that operate under the control of the Secretary of Homeland Security. The DHS consist of the United States Secret Service, the United States Customs Service, Bureau of Customs and Border protection, Bureau of Immigration and Customs Enforcement (Gaines, Miller, 2006). Each of these agencies perform different tasks but the main objective is the same; monitor international and foreign military and or terrorist activities to protect citizens from harm, to stop illegal transport and delivery of goods through customs, to stop illegal immigrants from entering the country illegally. The Department of the Treasury is also part of law enforcement. The primary office is known as the Internal Revenue Service (IRS) was formed in 1789 to regulate and control the federal government’s financial affairs. The IRS mints coins and prints paper money, borrows money, collects taxes from individuals and corporations, and pays all of the federal governments expenses (Gaines, Miller, 2006). The IRS Department also focuses on regulation and violations of the tax laws. The IRS has three branches the Examination Division that audits individual and corporation tax returns. The Collection Division attempts to collect owed and past due tax from individuals and corporations. The Criminal Investigation Division investigates possible tax fraud and tax evasion cases. Even though most people do not consider the IRS as part of law enforcement it very much is, it just mainly focuses mainly on money matters. However just like with all the agencies discussed in this article the IRS under Federal Law can carry a firearm and arrest you. Our personal survival depends on our law enforcement agencies to provide us with protection and community service and apprehend criminal suspects. If anyone of these organizations only had power to function within a specific community then the rest of the places outside of this community would probably see a dramatic rise in criminal behavior and activity. Without laws and law enforcement agencies the world would become disorderly and chaotic. Overall all of these law enforcement agencies carryout many of the same the responsibilities: providing citizens with community services, maintaining the peace, preventing and controlling crime, the apprehension of criminal suspects, and maintaining order of a community by the laws of our nation. Reference: Siegel, L. and Senna, J. (2008). Introduction to Criminal Justice, 11th ed. Thomson Learning Inc. Chapter 5. Gaines, L. and Miller, R. L. (2006). Criminal Justice in Action, the Core the 3rd ed. Thomson Learning Inc. Chapter 5.

Free Essays on La Z Boy

Group Project Financial Accounting La-Z-Boy Intro La-Z-boy Corporation was started in the 1920's and is now known as one of the largest best selling furniture industries to date. La-z-boy unlike other furniture companies which at the time focused on quality and customer satisfaction the most. The company also added a twist to there product by offering a year around piece of furniture which held the a certain name to it. Along with names the company also held furniture shows which consisted of circus mice, Merry-go rounds and Ferris wheels. Although the La-Z-Boy company profit tremendously form the time it started out of a garage, the company truly started to compete with other corporations when it went public in 1961. From 1961 to 1971 the La-z-Boy corporation witnessed astonishing sales when they introduced there reclining rocker chair which at the time increased sales from 1.1million dollars to 52.7million. In 1972 the company entered the market and Six hundred people brought more than 320,000 shares in over the counter trading. B y the end of the year La-z-Boy received annual sales of more than 152 million. At this time the company started to open up La-z-boy showcases which was meant to provide customers seeking genuine La-Z-Boy comfort furniture. To jump forward, La-z-boy hasn't let go of incorporating future technology in there chairs, from chairs that rocked, swiveled, glided and lifted to those that store items, massage and heat, and even have built in phones and laptop computers with internet hook up La-z-boy has no signs of not introducing quality products. By obtaining other furniture companies like Hammary, Kincaid, England/Corsair, Centurion, Sam More, Bauhaus USA, and more La-z-boy has gained access to newer markets and more customers. In 1997 the company exceeded 1 billion in annual sales and left its competition behind selling twice as much as it closets competitor. La-Z-boy a name synonymous with comfort will go... Free Essays on La Z Boy Free Essays on La Z Boy Group Project Financial Accounting La-Z-Boy Intro La-Z-boy Corporation was started in the 1920's and is now known as one of the largest best selling furniture industries to date. La-z-boy unlike other furniture companies which at the time focused on quality and customer satisfaction the most. The company also added a twist to there product by offering a year around piece of furniture which held the a certain name to it. Along with names the company also held furniture shows which consisted of circus mice, Merry-go rounds and Ferris wheels. Although the La-Z-Boy company profit tremendously form the time it started out of a garage, the company truly started to compete with other corporations when it went public in 1961. From 1961 to 1971 the La-z-Boy corporation witnessed astonishing sales when they introduced there reclining rocker chair which at the time increased sales from 1.1million dollars to 52.7million. In 1972 the company entered the market and Six hundred people brought more than 320,000 shares in over the counter trading. B y the end of the year La-z-Boy received annual sales of more than 152 million. At this time the company started to open up La-z-boy showcases which was meant to provide customers seeking genuine La-Z-Boy comfort furniture. To jump forward, La-z-boy hasn't let go of incorporating future technology in there chairs, from chairs that rocked, swiveled, glided and lifted to those that store items, massage and heat, and even have built in phones and laptop computers with internet hook up La-z-boy has no signs of not introducing quality products. By obtaining other furniture companies like Hammary, Kincaid, England/Corsair, Centurion, Sam More, Bauhaus USA, and more La-z-boy has gained access to newer markets and more customers. In 1997 the company exceeded 1 billion in annual sales and left its competition behind selling twice as much as it closets competitor. La-Z-boy a name synonymous with comfort will go...

Law Essay Example | Topics and Well Written Essays - 1250 words

Law - Essay Example However the manner in which such transactions are to be handled often depends on the laws of country A since it was the mother country and the constitution was laid out by both of the two conflicting sides. Controversies will arise and this is why a compromise between the two sides has to be struck in order to settle the dispute amicably. b) Before the war, State A had granted a mining concession to Dee Company for a 50-year period on land that is now within the territory of State B. That concession still has 20 years to run. State B claims that it is no longer valid. The mining concession awarded by state A to Dee Company for a 50-year period on land that is now within the territory of State B is not valid after the secession. Although the concession still has 20 years to run, State B’s claims that it is no longer valid is justified. If Dee Company is interested to continue mining on the territory of state B, then it has to sign a different concession with them. The two state s then have to strike an agreement on how to compensate Dee Company for the remaining part of the concession since by virtue of stopping the mining, they will have violated the terms of the contract. The mining company should also understand that the circumstance under which the contract was violated was beyond anybody’s control and that its renewal is the only way forward. (c) Before the war, State X had concluded a treaty with State A in which State X granted State A â€Å"most favored nation† trade status. State B now claims that it is entitled to the same treatment. State B is not entitled to the â€Å"most favored nation† status awarded by state X to A since it may not be able to fulfill certain conditions of the status. Additionally, it is a new country and should start looking for trade partners and not rely on the contracts made by state A because they are now two different

NAFTA - Managerial Economics Essay Example | Topics and Well Written Essays - 500 words

NAFTA - Managerial Economics - Essay Example In addition to labor concerns, opposition to NAFTA was strong among environmental groups, who contended that the treatys anti-pollution provisions were inadequate. To ease concerns that Mexicos low wage structure would cause U.S. companies to shift production to that country, and to ensure that Mexicos increasing industrialization under the Treaty would not create environmental pollution to a harmful degree, special side agreements were included in NAFTA. Under those agreements, the tri-national grouping agreed to establish appropriate commissions to handle labor and environmental issues. The commissions had the power to impose steep fines against any of the member governments that failed to consistently impose its laws. There have been criticisms regarding NAFTAs implementation of environmental protection provisions. Mexico, together with Canada, has been repeatedly cited for environmental malfeasance. Also, many observers have charged that the three governments have been lax in ensuring environmental safeguards since the agreement went into effect (Wikipedia). The NAFTA members are autonomous states that have yielded some of their sovereignty to establish and effectuate a treaty that would economically benefit some 365 million people in the region, but they have retained their power to determine their principal economic and social policies. In other words, the free trade arrangement does not include a supranational government that would enforce policies from the center. Consequently, each state is free to determine its policies, subject only to agreements it has committed itself to implement. An important consideration in this analysis is the fact that Mexico, unlike the United States (and Australia), is a signatory to the Kyoto Protocol to the United Nations Framework on Climate Change (UNFCCC) which aims to limit greenhouse gas emissions (GHG). The Kyoto protocol is concerned with the reduction of global pollution but requires less

Data Pre-processing Tool

Data Pre-processing Tool Chapter- 2 Real life data rarely comply with the necessities of various data mining tools. It is usually inconsistent and noisy. It may contain redundant attributes, unsuitable formats etc. Hence data has to be prepared vigilantly before the data mining actually starts. It is well known fact that success of a data mining algorithm is very much dependent on the quality of data processing. Data processing is one of the most important tasks in data mining. In this context it is natural that data pre-processing is a complicated task involving large data sets. Sometimes data pre-processing take more than 50% of the total time spent in solving the data mining problem. It is crucial for data miners to choose efficient data preprocessing technique for specific data set which can not only save processing time but also retain the quality of the data for data mining process. A data pre-processing tool should help miners with many data mining activates. For example, data may be provided in different formats as discussed in previous chapter (flat files, database files etc). Data files may also have different formats of values, calculation of derived attributes, data filters, joined data sets etc. Data mining process generally starts with understanding of data. In this stage pre-processing tools may help with data exploration and data discovery tasks. Data processing includes lots of tedious works, Data pre-processing generally consists of Data Cleaning Data Integration Data Transformation And Data Reduction. In this chapter we will study all these data pre-processing activities. 2.1 Data Understanding In Data understanding phase the first task is to collect initial data and then proceed with activities in order to get well known with data, to discover data quality problems, to discover first insight into the data or to identify interesting subset to form hypothesis for hidden information. The data understanding phase according to CRISP model can be shown in following . 2.1.1 Collect Initial Data The initial collection of data includes loading of data if required for data understanding. For instance, if specific tool is applied for data understanding, it makes great sense to load your data into this tool. This attempt possibly leads to initial data preparation steps. However if data is obtained from multiple data sources then integration is an additional issue. 2.1.2 Describe data Here the gross or surface properties of the gathered data are examined. 2.1.3 Explore data This task is required to handle the data mining questions, which may be addressed using querying, visualization and reporting. These include: Sharing of key attributes, for instance the goal attribute of a prediction task Relations between pairs or small numbers of attributes Results of simple aggregations Properties of important sub-populations Simple statistical analyses. 2.1.4 Verify data quality In this step quality of data is examined. It answers questions such as: Is the data complete (does it cover all the cases required)? Is it accurate or does it contains errors and if there are errors how common are they? Are there missing values in the data? If so how are they represented, where do they occur and how common are they? 2.2 Data Preprocessing Data preprocessing phase focus on the pre-processing steps that produce the data to be mined. Data preparation or preprocessing is one most important step in data mining. Industrial practice indicates that one data is well prepared; the mined results are much more accurate. This means this step is also a very critical fro success of data mining method. Among others, data preparation mainly involves data cleaning, data integration, data transformation, and reduction. 2.2.1 Data Cleaning Data cleaning is also known as data cleansing or scrubbing. It deals with detecting and removing inconsistencies and errors from data in order to get better quality data. While using a single data source such as flat files or databases data quality problems arises due to misspellings while data entry, missing information or other invalid data. While the data is taken from the integration of multiple data sources such as data warehouses, federated database systems or global web-based information systems, the requirement for data cleaning increases significantly. This is because the multiple sources may contain redundant data in different formats. Consolidation of different data formats abs elimination of redundant information becomes necessary in order to provide access to accurate and consistent data. Good quality data requires passing a set of quality criteria. Those criteria include: Accuracy: Accuracy is an aggregated value over the criteria of integrity, consistency and density. Integrity: Integrity is an aggregated value over the criteria of completeness and validity. Completeness: completeness is achieved by correcting data containing anomalies. Validity: Validity is approximated by the amount of data satisfying integrity constraints. Consistency: consistency concerns contradictions and syntactical anomalies in data. Uniformity: it is directly related to irregularities in data. Density: The density is the quotient of missing values in the data and the number of total values ought to be known. Uniqueness: uniqueness is related to the number of duplicates present in the data. 2.2.1.1 Terms Related to Data Cleaning Data cleaning: data cleaning is the process of detecting, diagnosing, and editing damaged data. Data editing: data editing means changing the value of data which are incorrect. Data flow: data flow is defined as passing of recorded information through succeeding information carriers. Inliers: Inliers are data values falling inside the projected range. Outlier: outliers are data value falling outside the projected range. Robust estimation: evaluation of statistical parameters, using methods that are less responsive to the effect of outliers than more conventional methods are called robust method. 2.2.1.2 Definition: Data Cleaning Data cleaning is a process used to identify imprecise, incomplete, or irrational data and then improving the quality through correction of detected errors and omissions. This process may include format checks Completeness checks Reasonableness checks Limit checks Review of the data to identify outliers or other errors Assessment of data by subject area experts (e.g. taxonomic specialists). By this process suspected records are flagged, documented and checked subsequently. And finally these suspected records can be corrected. Sometimes validation checks also involve checking for compliance against applicable standards, rules, and conventions. The general framework for data cleaning given as: Define and determine error types; Search and identify error instances; Correct the errors; Document error instances and error types; and Modify data entry procedures to reduce future errors. Data cleaning process is referred by different people by a number of terms. It is a matter of preference what one uses. These terms include: Error Checking, Error Detection, Data Validation, Data Cleaning, Data Cleansing, Data Scrubbing and Error Correction. We use Data Cleaning to encompass three sub-processes, viz. Data checking and error detection; Data validation; and Error correction. A fourth improvement of the error prevention processes could perhaps be added. 2.2.1.3 Problems with Data Here we just note some key problems with data Missing data : This problem occur because of two main reasons Data are absent in source where it is expected to be present. Some times data is present are not available in appropriately form Detecting missing data is usually straightforward and simpler. Erroneous data: This problem occurs when a wrong value is recorded for a real world value. Detection of erroneous data can be quite difficult. (For instance the incorrect spelling of a name) Duplicated data : This problem occur because of two reasons Repeated entry of same real world entity with some different values Some times a real world entity may have different identifications. Repeat records are regular and frequently easy to detect. The different identification of the same real world entities can be a very hard problem to identify and solve. Heterogeneities: When data from different sources are brought together in one analysis problem heterogeneity may occur. Heterogeneity could be Structural heterogeneity arises when the data structures reflect different business usage Semantic heterogeneity arises when the meaning of data is different n each system that is being combined Heterogeneities are usually very difficult to resolve since because they usually involve a lot of contextual data that is not well defined as metadata. Information dependencies in the relationship between the different sets of attribute are commonly present. Wrong cleaning mechanisms can further damage the information in the data. Various analysis tools handle these problems in different ways. Commercial offerings are available that assist the cleaning process, but these are often problem specific. Uncertainty in information systems is a well-recognized hard problem. In following a very simple examples of missing and erroneous data is shown Extensive support for data cleaning must be provided by data warehouses. Data warehouses have high probability of â€Å"dirty data† since they load and continuously refresh huge amounts of data from a variety of sources. Since these data warehouses are used for strategic decision making therefore the correctness of their data is important to avoid wrong decisions. The ETL (Extraction, Transformation, and Loading) process for building a data warehouse is illustrated in following . Data transformations are related with schema or data translation and integration, and with filtering and aggregating data to be stored in the data warehouse. All data cleaning is classically performed in a separate data performance area prior to loading the transformed data into the warehouse. A large number of tools of varying functionality are available to support these tasks, but often a significant portion of the cleaning and transformation work has to be done manually or by low-level programs that are difficult to write and maintain. A data cleaning method should assure following: It should identify and eliminate all major errors and inconsistencies in an individual data sources and also when integrating multiple sources. Data cleaning should be supported by tools to bound manual examination and programming effort and it should be extensible so that can cover additional sources. It should be performed in association with schema related data transformations based on metadata. Data cleaning mapping functions should be specified in a declarative way and be reusable for other data sources. 2.2.1.4 Data Cleaning: Phases 1. Analysis: To identify errors and inconsistencies in the database there is a need of detailed analysis, which involves both manual inspection and automated analysis programs. This reveals where (most of) the problems are present. 2. Defining Transformation and Mapping Rules: After discovering the problems, this phase are related with defining the manner by which we are going to automate the solutions to clean the data. We will find various problems that translate to a list of activities as a result of analysis phase. Example: Remove all entries for J. Smith because they are duplicates of John Smith Find entries with `bule in colour field and change these to `blue. Find all records where the Phone number field does not match the pattern (NNNNN NNNNNN). Further steps for cleaning this data are then applied. Etc †¦ 3. Verification: In this phase we check and assess the transformation plans made in phase- 2. Without this step, we may end up making the data dirtier rather than cleaner. Since data transformation is the main step that actually changes the data itself so there is a need to be sure that the applied transformations will do it correctly. Therefore test and examine the transformation plans very carefully. Example: Let we have a very thick C++ book where it says strict in all the places where it should say struct 4. Transformation: Now if it is sure that cleaning will be done correctly, then apply the transformation verified in last step. For large database, this task is supported by a variety of tools Backflow of Cleaned Data: In a data mining the main objective is to convert and move clean data into target system. This asks for a requirement to purify legacy data. Cleansing can be a complicated process depending on the technique chosen and has to be designed carefully to achieve the objective of removal of dirty data. Some methods to accomplish the task of data cleansing of legacy system include: n Automated data cleansing n Manual data cleansing n The combined cleansing process 2.2.1.5 Missing Values Data cleaning addresses a variety of data quality problems, including noise and outliers, inconsistent data, duplicate data, and missing values. Missing values is one important problem to be addressed. Missing value problem occurs because many tuples may have no record for several attributes. For Example there is a customer sales database consisting of a whole bunch of records (lets say around 100,000) where some of the records have certain fields missing. Lets say customer income in sales data may be missing. Goal here is to find a way to predict what the missing data values should be (so that these can be filled) based on the existing data. Missing data may be due to following reasons Equipment malfunction Inconsistent with other recorded data and thus deleted Data not entered due to misunderstanding Certain data may not be considered important at the time of entry Not register history or changes of the data How to Handle Missing Values? Dealing with missing values is a regular question that has to do with the actual meaning of the data. There are various methods for handling missing entries 1. Ignore the data row. One solution of missing values is to just ignore the entire data row. This is generally done when the class label is not there (here we are assuming that the data mining goal is classification), or many attributes are missing from the row (not just one). But if the percentage of such rows is high we will definitely get a poor performance. 2. Use a global constant to fill in for missing values. We can fill in a global constant for missing values such as unknown, N/A or minus infinity. This is done because at times is just doesnt make sense to try and predict the missing value. For example if in customer sales database if, say, office address is missing for some, filling it in doesnt make much sense. This method is simple but is not full proof. 3. Use attribute mean. Let say if the average income of a a family is X you can use that value to replace missing income values in the customer sales database. 4. Use attribute mean for all samples belonging to the same class. Lets say you have a cars pricing DB that, among other things, classifies cars to Luxury and Low budget and youre dealing with missing values in the cost field. Replacing missing cost of a luxury car with the average cost of all luxury cars is probably more accurate then the value youd get if you factor in the low budget 5. Use data mining algorithm to predict the value. The value can be determined using regression, inference based tools using Bayesian formalism, decision trees, clustering algorithms etc. 2.2.1.6 Noisy Data Noise can be defined as a random error or variance in a measured variable. Due to randomness it is very difficult to follow a strategy for noise removal from the data. Real world data is not always faultless. It can suffer from corruption which may impact the interpretations of the data, models created from the data, and decisions made based on the data. Incorrect attribute values could be present because of following reasons Faulty data collection instruments Data entry problems Duplicate records Incomplete data: Inconsistent data Incorrect processing Data transmission problems Technology limitation. Inconsistency in naming convention Outliers How to handle Noisy Data? The methods for removing noise from data are as follows. 1. Binning: this approach first sort data and partition it into (equal-frequency) bins then one can smooth it using- Bin means, smooth using bin median, smooth using bin boundaries, etc. 2. Regression: in this method smoothing is done by fitting the data into regression functions. 3. Clustering: clustering detect and remove outliers from the data. 4. Combined computer and human inspection: in this approach computer detects suspicious values which are then checked by human experts (e.g., this approach deal with possible outliers).. Following methods are explained in detail as follows: Binning: Data preparation activity that converts continuous data to discrete data by replacing a value from a continuous range with a bin identifier, where each bin represents a range of values. For instance, age can be changed to bins such as 20 or under, 21-40, 41-65 and over 65. Binning methods smooth a sorted data set by consulting values around it. This is therefore called local smoothing. Let consider a binning example Binning Methods n Equal-width (distance) partitioning Divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B-A)/N. The most straightforward, but outliers may dominate presentation Skewed data is not handled well n Equal-depth (frequency) partitioning 1. It divides the range (values of a given attribute) into N intervals, each containing approximately same number of samples (elements) 2. Good data scaling 3. Managing categorical attributes can be tricky. n Smooth by bin means- Each bin value is replaced by the mean of values n Smooth by bin medians- Each bin value is replaced by the median of values n Smooth by bin boundaries Each bin value is replaced by the closest boundary value Example Let Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 n Partition into equal-frequency (equi-depth) bins: o Bin 1: 4, 8, 9, 15 o Bin 2: 21, 21, 24, 25 o Bin 3: 26, 28, 29, 34 n Smoothing by bin means: o Bin 1: 9, 9, 9, 9 ( for example mean of 4, 8, 9, 15 is 9) o Bin 2: 23, 23, 23, 23 o Bin 3: 29, 29, 29, 29 n Smoothing by bin boundaries: o Bin 1: 4, 4, 4, 15 o Bin 2: 21, 21, 25, 25 o Bin 3: 26, 26, 26, 34 Regression: Regression is a DM technique used to fit an equation to a dataset. The simplest form of regression is linear regression which uses the formula of a straight line (y = b+ wx) and determines the suitable values for b and w to predict the value of y based upon a given value of x. Sophisticated techniques, such as multiple regression, permit the use of more than one input variable and allow for the fitting of more complex models, such as a quadratic equation. Regression is further described in subsequent chapter while discussing predictions. Clustering: clustering is a method of grouping data into different groups , so that data in each group share similar trends and patterns. Clustering constitute a major class of data mining algorithms. These algorithms automatically partitions the data space into set of regions or cluster. The goal of the process is to find all set of similar examples in data, in some optimal fashion. Following shows three clusters. Values that fall outsid e the cluster are outliers. 4. Combined computer and human inspection: These methods find the suspicious values using the computer programs and then they are verified by human experts. By this process all outliers are checked. 2.2.1.7 Data cleaning as a process Data cleaning is the process of Detecting, Diagnosing, and Editing Data. Data cleaning is a three stage method involving repeated cycle of screening, diagnosing, and editing of suspected data abnormalities. Many data errors are detected by the way during study activities. However, it is more efficient to discover inconsistencies by actively searching for them in a planned manner. It is not always right away clear whether a data point is erroneous. Many times it requires careful examination. Likewise, missing values require additional check. Therefore, predefined rules for dealing with errors and true missing and extreme values are part of good practice. One can monitor for suspect features in survey questionnaires, databases, or analysis data. In small studies, with the examiner intimately involved at all stages, there may be small or no difference between a database and an analysis dataset. During as well as after treatment, the diagnostic and treatment phases of cleaning need insight into the sources and types of errors at all stages of the study. Data flow concept is therefore crucial in this respect. After measurement the research data go through repeated steps of- entering into information carriers, extracted, and transferred to other carriers, edited, selected, transformed, summarized, and presented. It is essential to understand that errors can occur at any stage of the data flow, including during data cleaning itself. Most of these problems are due to human error. Inaccuracy of a single data point and measurement may be tolerable, and associated to the inherent technological error of the measurement device. Therefore the process of data clenaning mus focus on those errors that are beyond small technical variations and that form a major shift within or beyond the population distribution. In turn, it must be based on understanding of technical errors and expected ranges of normal values. Some errors are worthy of higher priority, but which ones are most significant is highly study-specific. For instance in most medical epidemiological studies, errors that need to be cleaned, at all costs, include missing gender, gender misspecification, birth date or examination date errors, duplications or merging of records, and biologically impossible results. Another example is in nutrition studies, date errors lead to age errors, which in turn lead to errors in weight-for-age scoring and, further, to misclassification of subjects as under- or overweight. Errors of sex and date are particularly important because they contaminate derived variables. Prioritization is essential if the study is under time pressures or if resources for data cleaning are limited. 2.2.2 Data Integration This is a process of taking data from one or more sources and mapping it, field by field, onto a new data structure. Idea is to combine data from multiple sources into a coherent form. Various data mining projects requires data from multiple sources because n Data may be distributed over different databases or data warehouses. (for example an epidemiological study that needs information about hospital admissions and car accidents) n Sometimes data may be required from different geographic distributions, or there may be need for historical data. (e.g. integrate historical data into a new data warehouse) n There may be a necessity of enhancement of data with additional (external) data. (for improving data mining precision) 2.2.2.1 Data Integration Issues There are number of issues in data integrations. Consider two database tables. Imagine two database tables Database Table-1 Database Table-2 In integration of there two tables there are variety of issues involved such as 1. The same attribute may have different names (for example in above tables Name and Given Name are same attributes with different names) 2. An attribute may be derived from another (for example attribute Age is derived from attribute DOB) 3. Attributes might be redundant( For example attribute PID is redundant) 4. Values in attributes might be different (for example for PID 4791 values in second and third field are different in both the tables) 5. Duplicate records under different keys( there is a possibility of replication of same record with different key values) Therefore schema integration and object matching can be trickier. Question here is how equivalent entities from different sources are matched? This problem is known as entity identification problem. Conflicts have to be detected and resolved. Integration becomes easier if unique entity keys are available in all the data sets (or tables) to be linked. Metadata can help in schema integration (example of metadata for each attribute includes the name, meaning, data type and range of values permitted for the attribute) 2.2.2.1 Redundancy Redundancy is another important issue in data integration. Two given attribute (such as DOB and age for instance in give table) may be redundant if one is derived form the other attribute or set of attributes. Inconsistencies in attribute or dimension naming can lead to redundancies in the given data sets. Handling Redundant Data We can handle data redundancy problems by following ways n Use correlation analysis n Different coding / representation has to be considered (e.g. metric / imperial measures) n Careful (manual) integration of the data can reduce or prevent redundancies (and inconsistencies) n De-duplication (also called internal data linkage) o If no unique entity keys are available o Analysis of values in attributes to find duplicates n Process redundant and inconsistent data (easy if values are the same) o Delete one of the values o Average values (only for numerical attributes) o Take majority values (if more than 2 duplicates and some values are the same) Correlation analysis is explained in detail here. Correlation analysis (also called Pearsons product moment coefficient): some redundancies can be detected by using correlation analysis. Given two attributes, such analysis can measure how strong one attribute implies another. For numerical attribute we can compute correlation coefficient of two attributes A and B to evaluate the correlation between them. This is given by Where n n is the number of tuples, n and are the respective means of A and B n ÏÆ'A and ÏÆ'B are the respective standard deviation of A and B n ÃŽ £(AB) is the sum of the AB cross-product. a. If -1 b. If rA, B is equal to zero it indicates A and B are independent of each other and there is no correlation between them. c. If rA, B is less than zero then A and B are negatively correlated. , where if value of one attribute increases value of another attribute decreases. This means that one attribute discourages another attribute. It is important to note that correlation does not imply causality. That is, if A and B are correlated, this does not essentially mean that A causes B or that B causes A. for example in analyzing a demographic database, we may find that attribute representing number of accidents and the number of car theft in a region are correlated. This does not mean that one is related to another. Both may be related to third attribute, namely population. For discrete data, a correlation relation between two attributes, can be discovered by a χ ²(chi-square) test. Let A has c distinct values a1,a2,†¦Ã¢â‚¬ ¦ac and B has r different values namely b1,b2,†¦Ã¢â‚¬ ¦br The data tuple described by A and B are shown as contingency table, with c values of A (making up columns) and r values of B( making up rows). Each and every (Ai, Bj) cell in table has. X^2 = sum_{i=1}^{r} sum_{j=1}^{c} {(O_{i,j} E_{i,j})^2 over E_{i,j}} . Where n Oi, j is the observed frequency (i.e. actual count) of joint event (Ai, Bj) and n Ei, j is the expected frequency which can be computed as E_{i,j}=frac{sum_{k=1}^{c} O_{i,k} sum_{k=1}^{r} O_{k,j}}{N} , , Where n N is number of data tuple n Oi,k is number of tuples having value ai for A n Ok,j is number of tuples having value bj for B The larger the χ ² value, the more likely the variables are related. The cells that contribute the most to the χ ² value are those whose actual count is very different from the expected count Chi-Square Calculation: An Example Suppose a group of 1,500 people were surveyed. The gender of each person was noted. Each person has polled their preferred type of reading material as fiction or non-fiction. The observed frequency of each possible joint event is summarized in following table.( number in parenthesis are expected frequencies) . Calculate chi square. Play chess Not play chess Sum (row) Like science fiction 250(90) 200(360) 450 Not like science fiction 50(210) 1000(840) 1050 Sum(col.) 300 1200 1500 E11 = count (male)*count(fiction)/N = 300 * 450 / 1500 =90 and so on For this table the degree of freedom are (2-1)(2-1) =1 as table is 2X2. for 1 degree of freedom , the χ ² value needed to reject the hypothesis at the 0.001 significance level is 10.828 (taken from the table of upper percentage point of the χ ² distribution typically available in any statistic text book). Since the computed value is above this, we can reject the hypothesis that gender and preferred reading are independent and conclude that two attributes are strongly correlated for given group. Duplication must also be detected at the tuple level. The use of renormalized tables is also a source of redundancies. Redundancies may further lead to data inconsistencies (due to updating some but not others). 2.2.2.2 Detection and resolution of data value conflicts Another significant issue in data integration is the discovery and resolution of data value conflicts. For example, for the same entity, attribute values from different sources may differ. For example weight can be stored in metric unit in one source and British imperial unit in another source. For instance, for a hotel cha Data Pre-processing Tool Data Pre-processing Tool Chapter- 2 Real life data rarely comply with the necessities of various data mining tools. It is usually inconsistent and noisy. It may contain redundant attributes, unsuitable formats etc. Hence data has to be prepared vigilantly before the data mining actually starts. It is well known fact that success of a data mining algorithm is very much dependent on the quality of data processing. Data processing is one of the most important tasks in data mining. In this context it is natural that data pre-processing is a complicated task involving large data sets. Sometimes data pre-processing take more than 50% of the total time spent in solving the data mining problem. It is crucial for data miners to choose efficient data preprocessing technique for specific data set which can not only save processing time but also retain the quality of the data for data mining process. A data pre-processing tool should help miners with many data mining activates. For example, data may be provided in different formats as discussed in previous chapter (flat files, database files etc). Data files may also have different formats of values, calculation of derived attributes, data filters, joined data sets etc. Data mining process generally starts with understanding of data. In this stage pre-processing tools may help with data exploration and data discovery tasks. Data processing includes lots of tedious works, Data pre-processing generally consists of Data Cleaning Data Integration Data Transformation And Data Reduction. In this chapter we will study all these data pre-processing activities. 2.1 Data Understanding In Data understanding phase the first task is to collect initial data and then proceed with activities in order to get well known with data, to discover data quality problems, to discover first insight into the data or to identify interesting subset to form hypothesis for hidden information. The data understanding phase according to CRISP model can be shown in following . 2.1.1 Collect Initial Data The initial collection of data includes loading of data if required for data understanding. For instance, if specific tool is applied for data understanding, it makes great sense to load your data into this tool. This attempt possibly leads to initial data preparation steps. However if data is obtained from multiple data sources then integration is an additional issue. 2.1.2 Describe data Here the gross or surface properties of the gathered data are examined. 2.1.3 Explore data This task is required to handle the data mining questions, which may be addressed using querying, visualization and reporting. These include: Sharing of key attributes, for instance the goal attribute of a prediction task Relations between pairs or small numbers of attributes Results of simple aggregations Properties of important sub-populations Simple statistical analyses. 2.1.4 Verify data quality In this step quality of data is examined. It answers questions such as: Is the data complete (does it cover all the cases required)? Is it accurate or does it contains errors and if there are errors how common are they? Are there missing values in the data? If so how are they represented, where do they occur and how common are they? 2.2 Data Preprocessing Data preprocessing phase focus on the pre-processing steps that produce the data to be mined. Data preparation or preprocessing is one most important step in data mining. Industrial practice indicates that one data is well prepared; the mined results are much more accurate. This means this step is also a very critical fro success of data mining method. Among others, data preparation mainly involves data cleaning, data integration, data transformation, and reduction. 2.2.1 Data Cleaning Data cleaning is also known as data cleansing or scrubbing. It deals with detecting and removing inconsistencies and errors from data in order to get better quality data. While using a single data source such as flat files or databases data quality problems arises due to misspellings while data entry, missing information or other invalid data. While the data is taken from the integration of multiple data sources such as data warehouses, federated database systems or global web-based information systems, the requirement for data cleaning increases significantly. This is because the multiple sources may contain redundant data in different formats. Consolidation of different data formats abs elimination of redundant information becomes necessary in order to provide access to accurate and consistent data. Good quality data requires passing a set of quality criteria. Those criteria include: Accuracy: Accuracy is an aggregated value over the criteria of integrity, consistency and density. Integrity: Integrity is an aggregated value over the criteria of completeness and validity. Completeness: completeness is achieved by correcting data containing anomalies. Validity: Validity is approximated by the amount of data satisfying integrity constraints. Consistency: consistency concerns contradictions and syntactical anomalies in data. Uniformity: it is directly related to irregularities in data. Density: The density is the quotient of missing values in the data and the number of total values ought to be known. Uniqueness: uniqueness is related to the number of duplicates present in the data. 2.2.1.1 Terms Related to Data Cleaning Data cleaning: data cleaning is the process of detecting, diagnosing, and editing damaged data. Data editing: data editing means changing the value of data which are incorrect. Data flow: data flow is defined as passing of recorded information through succeeding information carriers. Inliers: Inliers are data values falling inside the projected range. Outlier: outliers are data value falling outside the projected range. Robust estimation: evaluation of statistical parameters, using methods that are less responsive to the effect of outliers than more conventional methods are called robust method. 2.2.1.2 Definition: Data Cleaning Data cleaning is a process used to identify imprecise, incomplete, or irrational data and then improving the quality through correction of detected errors and omissions. This process may include format checks Completeness checks Reasonableness checks Limit checks Review of the data to identify outliers or other errors Assessment of data by subject area experts (e.g. taxonomic specialists). By this process suspected records are flagged, documented and checked subsequently. And finally these suspected records can be corrected. Sometimes validation checks also involve checking for compliance against applicable standards, rules, and conventions. The general framework for data cleaning given as: Define and determine error types; Search and identify error instances; Correct the errors; Document error instances and error types; and Modify data entry procedures to reduce future errors. Data cleaning process is referred by different people by a number of terms. It is a matter of preference what one uses. These terms include: Error Checking, Error Detection, Data Validation, Data Cleaning, Data Cleansing, Data Scrubbing and Error Correction. We use Data Cleaning to encompass three sub-processes, viz. Data checking and error detection; Data validation; and Error correction. A fourth improvement of the error prevention processes could perhaps be added. 2.2.1.3 Problems with Data Here we just note some key problems with data Missing data : This problem occur because of two main reasons Data are absent in source where it is expected to be present. Some times data is present are not available in appropriately form Detecting missing data is usually straightforward and simpler. Erroneous data: This problem occurs when a wrong value is recorded for a real world value. Detection of erroneous data can be quite difficult. (For instance the incorrect spelling of a name) Duplicated data : This problem occur because of two reasons Repeated entry of same real world entity with some different values Some times a real world entity may have different identifications. Repeat records are regular and frequently easy to detect. The different identification of the same real world entities can be a very hard problem to identify and solve. Heterogeneities: When data from different sources are brought together in one analysis problem heterogeneity may occur. Heterogeneity could be Structural heterogeneity arises when the data structures reflect different business usage Semantic heterogeneity arises when the meaning of data is different n each system that is being combined Heterogeneities are usually very difficult to resolve since because they usually involve a lot of contextual data that is not well defined as metadata. Information dependencies in the relationship between the different sets of attribute are commonly present. Wrong cleaning mechanisms can further damage the information in the data. Various analysis tools handle these problems in different ways. Commercial offerings are available that assist the cleaning process, but these are often problem specific. Uncertainty in information systems is a well-recognized hard problem. In following a very simple examples of missing and erroneous data is shown Extensive support for data cleaning must be provided by data warehouses. Data warehouses have high probability of â€Å"dirty data† since they load and continuously refresh huge amounts of data from a variety of sources. Since these data warehouses are used for strategic decision making therefore the correctness of their data is important to avoid wrong decisions. The ETL (Extraction, Transformation, and Loading) process for building a data warehouse is illustrated in following . Data transformations are related with schema or data translation and integration, and with filtering and aggregating data to be stored in the data warehouse. All data cleaning is classically performed in a separate data performance area prior to loading the transformed data into the warehouse. A large number of tools of varying functionality are available to support these tasks, but often a significant portion of the cleaning and transformation work has to be done manually or by low-level programs that are difficult to write and maintain. A data cleaning method should assure following: It should identify and eliminate all major errors and inconsistencies in an individual data sources and also when integrating multiple sources. Data cleaning should be supported by tools to bound manual examination and programming effort and it should be extensible so that can cover additional sources. It should be performed in association with schema related data transformations based on metadata. Data cleaning mapping functions should be specified in a declarative way and be reusable for other data sources. 2.2.1.4 Data Cleaning: Phases 1. Analysis: To identify errors and inconsistencies in the database there is a need of detailed analysis, which involves both manual inspection and automated analysis programs. This reveals where (most of) the problems are present. 2. Defining Transformation and Mapping Rules: After discovering the problems, this phase are related with defining the manner by which we are going to automate the solutions to clean the data. We will find various problems that translate to a list of activities as a result of analysis phase. Example: Remove all entries for J. Smith because they are duplicates of John Smith Find entries with `bule in colour field and change these to `blue. Find all records where the Phone number field does not match the pattern (NNNNN NNNNNN). Further steps for cleaning this data are then applied. Etc †¦ 3. Verification: In this phase we check and assess the transformation plans made in phase- 2. Without this step, we may end up making the data dirtier rather than cleaner. Since data transformation is the main step that actually changes the data itself so there is a need to be sure that the applied transformations will do it correctly. Therefore test and examine the transformation plans very carefully. Example: Let we have a very thick C++ book where it says strict in all the places where it should say struct 4. Transformation: Now if it is sure that cleaning will be done correctly, then apply the transformation verified in last step. For large database, this task is supported by a variety of tools Backflow of Cleaned Data: In a data mining the main objective is to convert and move clean data into target system. This asks for a requirement to purify legacy data. Cleansing can be a complicated process depending on the technique chosen and has to be designed carefully to achieve the objective of removal of dirty data. Some methods to accomplish the task of data cleansing of legacy system include: n Automated data cleansing n Manual data cleansing n The combined cleansing process 2.2.1.5 Missing Values Data cleaning addresses a variety of data quality problems, including noise and outliers, inconsistent data, duplicate data, and missing values. Missing values is one important problem to be addressed. Missing value problem occurs because many tuples may have no record for several attributes. For Example there is a customer sales database consisting of a whole bunch of records (lets say around 100,000) where some of the records have certain fields missing. Lets say customer income in sales data may be missing. Goal here is to find a way to predict what the missing data values should be (so that these can be filled) based on the existing data. Missing data may be due to following reasons Equipment malfunction Inconsistent with other recorded data and thus deleted Data not entered due to misunderstanding Certain data may not be considered important at the time of entry Not register history or changes of the data How to Handle Missing Values? Dealing with missing values is a regular question that has to do with the actual meaning of the data. There are various methods for handling missing entries 1. Ignore the data row. One solution of missing values is to just ignore the entire data row. This is generally done when the class label is not there (here we are assuming that the data mining goal is classification), or many attributes are missing from the row (not just one). But if the percentage of such rows is high we will definitely get a poor performance. 2. Use a global constant to fill in for missing values. We can fill in a global constant for missing values such as unknown, N/A or minus infinity. This is done because at times is just doesnt make sense to try and predict the missing value. For example if in customer sales database if, say, office address is missing for some, filling it in doesnt make much sense. This method is simple but is not full proof. 3. Use attribute mean. Let say if the average income of a a family is X you can use that value to replace missing income values in the customer sales database. 4. Use attribute mean for all samples belonging to the same class. Lets say you have a cars pricing DB that, among other things, classifies cars to Luxury and Low budget and youre dealing with missing values in the cost field. Replacing missing cost of a luxury car with the average cost of all luxury cars is probably more accurate then the value youd get if you factor in the low budget 5. Use data mining algorithm to predict the value. The value can be determined using regression, inference based tools using Bayesian formalism, decision trees, clustering algorithms etc. 2.2.1.6 Noisy Data Noise can be defined as a random error or variance in a measured variable. Due to randomness it is very difficult to follow a strategy for noise removal from the data. Real world data is not always faultless. It can suffer from corruption which may impact the interpretations of the data, models created from the data, and decisions made based on the data. Incorrect attribute values could be present because of following reasons Faulty data collection instruments Data entry problems Duplicate records Incomplete data: Inconsistent data Incorrect processing Data transmission problems Technology limitation. Inconsistency in naming convention Outliers How to handle Noisy Data? The methods for removing noise from data are as follows. 1. Binning: this approach first sort data and partition it into (equal-frequency) bins then one can smooth it using- Bin means, smooth using bin median, smooth using bin boundaries, etc. 2. Regression: in this method smoothing is done by fitting the data into regression functions. 3. Clustering: clustering detect and remove outliers from the data. 4. Combined computer and human inspection: in this approach computer detects suspicious values which are then checked by human experts (e.g., this approach deal with possible outliers).. Following methods are explained in detail as follows: Binning: Data preparation activity that converts continuous data to discrete data by replacing a value from a continuous range with a bin identifier, where each bin represents a range of values. For instance, age can be changed to bins such as 20 or under, 21-40, 41-65 and over 65. Binning methods smooth a sorted data set by consulting values around it. This is therefore called local smoothing. Let consider a binning example Binning Methods n Equal-width (distance) partitioning Divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B-A)/N. The most straightforward, but outliers may dominate presentation Skewed data is not handled well n Equal-depth (frequency) partitioning 1. It divides the range (values of a given attribute) into N intervals, each containing approximately same number of samples (elements) 2. Good data scaling 3. Managing categorical attributes can be tricky. n Smooth by bin means- Each bin value is replaced by the mean of values n Smooth by bin medians- Each bin value is replaced by the median of values n Smooth by bin boundaries Each bin value is replaced by the closest boundary value Example Let Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 n Partition into equal-frequency (equi-depth) bins: o Bin 1: 4, 8, 9, 15 o Bin 2: 21, 21, 24, 25 o Bin 3: 26, 28, 29, 34 n Smoothing by bin means: o Bin 1: 9, 9, 9, 9 ( for example mean of 4, 8, 9, 15 is 9) o Bin 2: 23, 23, 23, 23 o Bin 3: 29, 29, 29, 29 n Smoothing by bin boundaries: o Bin 1: 4, 4, 4, 15 o Bin 2: 21, 21, 25, 25 o Bin 3: 26, 26, 26, 34 Regression: Regression is a DM technique used to fit an equation to a dataset. The simplest form of regression is linear regression which uses the formula of a straight line (y = b+ wx) and determines the suitable values for b and w to predict the value of y based upon a given value of x. Sophisticated techniques, such as multiple regression, permit the use of more than one input variable and allow for the fitting of more complex models, such as a quadratic equation. Regression is further described in subsequent chapter while discussing predictions. Clustering: clustering is a method of grouping data into different groups , so that data in each group share similar trends and patterns. Clustering constitute a major class of data mining algorithms. These algorithms automatically partitions the data space into set of regions or cluster. The goal of the process is to find all set of similar examples in data, in some optimal fashion. Following shows three clusters. Values that fall outsid e the cluster are outliers. 4. Combined computer and human inspection: These methods find the suspicious values using the computer programs and then they are verified by human experts. By this process all outliers are checked. 2.2.1.7 Data cleaning as a process Data cleaning is the process of Detecting, Diagnosing, and Editing Data. Data cleaning is a three stage method involving repeated cycle of screening, diagnosing, and editing of suspected data abnormalities. Many data errors are detected by the way during study activities. However, it is more efficient to discover inconsistencies by actively searching for them in a planned manner. It is not always right away clear whether a data point is erroneous. Many times it requires careful examination. Likewise, missing values require additional check. Therefore, predefined rules for dealing with errors and true missing and extreme values are part of good practice. One can monitor for suspect features in survey questionnaires, databases, or analysis data. In small studies, with the examiner intimately involved at all stages, there may be small or no difference between a database and an analysis dataset. During as well as after treatment, the diagnostic and treatment phases of cleaning need insight into the sources and types of errors at all stages of the study. Data flow concept is therefore crucial in this respect. After measurement the research data go through repeated steps of- entering into information carriers, extracted, and transferred to other carriers, edited, selected, transformed, summarized, and presented. It is essential to understand that errors can occur at any stage of the data flow, including during data cleaning itself. Most of these problems are due to human error. Inaccuracy of a single data point and measurement may be tolerable, and associated to the inherent technological error of the measurement device. Therefore the process of data clenaning mus focus on those errors that are beyond small technical variations and that form a major shift within or beyond the population distribution. In turn, it must be based on understanding of technical errors and expected ranges of normal values. Some errors are worthy of higher priority, but which ones are most significant is highly study-specific. For instance in most medical epidemiological studies, errors that need to be cleaned, at all costs, include missing gender, gender misspecification, birth date or examination date errors, duplications or merging of records, and biologically impossible results. Another example is in nutrition studies, date errors lead to age errors, which in turn lead to errors in weight-for-age scoring and, further, to misclassification of subjects as under- or overweight. Errors of sex and date are particularly important because they contaminate derived variables. Prioritization is essential if the study is under time pressures or if resources for data cleaning are limited. 2.2.2 Data Integration This is a process of taking data from one or more sources and mapping it, field by field, onto a new data structure. Idea is to combine data from multiple sources into a coherent form. Various data mining projects requires data from multiple sources because n Data may be distributed over different databases or data warehouses. (for example an epidemiological study that needs information about hospital admissions and car accidents) n Sometimes data may be required from different geographic distributions, or there may be need for historical data. (e.g. integrate historical data into a new data warehouse) n There may be a necessity of enhancement of data with additional (external) data. (for improving data mining precision) 2.2.2.1 Data Integration Issues There are number of issues in data integrations. Consider two database tables. Imagine two database tables Database Table-1 Database Table-2 In integration of there two tables there are variety of issues involved such as 1. The same attribute may have different names (for example in above tables Name and Given Name are same attributes with different names) 2. An attribute may be derived from another (for example attribute Age is derived from attribute DOB) 3. Attributes might be redundant( For example attribute PID is redundant) 4. Values in attributes might be different (for example for PID 4791 values in second and third field are different in both the tables) 5. Duplicate records under different keys( there is a possibility of replication of same record with different key values) Therefore schema integration and object matching can be trickier. Question here is how equivalent entities from different sources are matched? This problem is known as entity identification problem. Conflicts have to be detected and resolved. Integration becomes easier if unique entity keys are available in all the data sets (or tables) to be linked. Metadata can help in schema integration (example of metadata for each attribute includes the name, meaning, data type and range of values permitted for the attribute) 2.2.2.1 Redundancy Redundancy is another important issue in data integration. Two given attribute (such as DOB and age for instance in give table) may be redundant if one is derived form the other attribute or set of attributes. Inconsistencies in attribute or dimension naming can lead to redundancies in the given data sets. Handling Redundant Data We can handle data redundancy problems by following ways n Use correlation analysis n Different coding / representation has to be considered (e.g. metric / imperial measures) n Careful (manual) integration of the data can reduce or prevent redundancies (and inconsistencies) n De-duplication (also called internal data linkage) o If no unique entity keys are available o Analysis of values in attributes to find duplicates n Process redundant and inconsistent data (easy if values are the same) o Delete one of the values o Average values (only for numerical attributes) o Take majority values (if more than 2 duplicates and some values are the same) Correlation analysis is explained in detail here. Correlation analysis (also called Pearsons product moment coefficient): some redundancies can be detected by using correlation analysis. Given two attributes, such analysis can measure how strong one attribute implies another. For numerical attribute we can compute correlation coefficient of two attributes A and B to evaluate the correlation between them. This is given by Where n n is the number of tuples, n and are the respective means of A and B n ÏÆ'A and ÏÆ'B are the respective standard deviation of A and B n ÃŽ £(AB) is the sum of the AB cross-product. a. If -1 b. If rA, B is equal to zero it indicates A and B are independent of each other and there is no correlation between them. c. If rA, B is less than zero then A and B are negatively correlated. , where if value of one attribute increases value of another attribute decreases. This means that one attribute discourages another attribute. It is important to note that correlation does not imply causality. That is, if A and B are correlated, this does not essentially mean that A causes B or that B causes A. for example in analyzing a demographic database, we may find that attribute representing number of accidents and the number of car theft in a region are correlated. This does not mean that one is related to another. Both may be related to third attribute, namely population. For discrete data, a correlation relation between two attributes, can be discovered by a χ ²(chi-square) test. Let A has c distinct values a1,a2,†¦Ã¢â‚¬ ¦ac and B has r different values namely b1,b2,†¦Ã¢â‚¬ ¦br The data tuple described by A and B are shown as contingency table, with c values of A (making up columns) and r values of B( making up rows). Each and every (Ai, Bj) cell in table has. X^2 = sum_{i=1}^{r} sum_{j=1}^{c} {(O_{i,j} E_{i,j})^2 over E_{i,j}} . Where n Oi, j is the observed frequency (i.e. actual count) of joint event (Ai, Bj) and n Ei, j is the expected frequency which can be computed as E_{i,j}=frac{sum_{k=1}^{c} O_{i,k} sum_{k=1}^{r} O_{k,j}}{N} , , Where n N is number of data tuple n Oi,k is number of tuples having value ai for A n Ok,j is number of tuples having value bj for B The larger the χ ² value, the more likely the variables are related. The cells that contribute the most to the χ ² value are those whose actual count is very different from the expected count Chi-Square Calculation: An Example Suppose a group of 1,500 people were surveyed. The gender of each person was noted. Each person has polled their preferred type of reading material as fiction or non-fiction. The observed frequency of each possible joint event is summarized in following table.( number in parenthesis are expected frequencies) . Calculate chi square. Play chess Not play chess Sum (row) Like science fiction 250(90) 200(360) 450 Not like science fiction 50(210) 1000(840) 1050 Sum(col.) 300 1200 1500 E11 = count (male)*count(fiction)/N = 300 * 450 / 1500 =90 and so on For this table the degree of freedom are (2-1)(2-1) =1 as table is 2X2. for 1 degree of freedom , the χ ² value needed to reject the hypothesis at the 0.001 significance level is 10.828 (taken from the table of upper percentage point of the χ ² distribution typically available in any statistic text book). Since the computed value is above this, we can reject the hypothesis that gender and preferred reading are independent and conclude that two attributes are strongly correlated for given group. Duplication must also be detected at the tuple level. The use of renormalized tables is also a source of redundancies. Redundancies may further lead to data inconsistencies (due to updating some but not others). 2.2.2.2 Detection and resolution of data value conflicts Another significant issue in data integration is the discovery and resolution of data value conflicts. For example, for the same entity, attribute values from different sources may differ. For example weight can be stored in metric unit in one source and British imperial unit in another source. For instance, for a hotel cha