Data warehousing and data mining

Data warehousing and data mining Abstract This paper aims to discuss about data warehousing and data mining, the tools and techniques of data mining and data warehousing as well as the benefits of practicing the concept to the organisations. It also includes the trends and application in data warehouse and data mining in current business communities. Keywords Database, data warehouse, data mining, database management. Introduction Organisation uses information systems to record and retrieve data from daily transactions. The information systems via the database that link to it provides valuable data for making important and strategic decisions in regards to the well-being of a company. An organisation can predict the expectation that is yet to come from the data that they possessed. The data can also be used to provide possible solutions to overcome the problems that they faced, and even, they can use the data to obtain competitive advantage in their business environment. Database has reduces, if not in some place, vanish the old method of storing and keeping the information, that is, through the usage of the traditional filing system. The change towards digitization of data and the establishment of data repository has created a new term in the field of information systems, new position in the organisation, and a new way of doing business and daily transactions in human life. This paper will discuss further about the two terminologies which is data warehouse and data mining from the perspective of database management in the organisation. At the same time, this paper will also include some cases and issues about data warehouse in the organisation according to real situation based on the literatures. According to William H. Inmon, data warehouse is a set of integrated, subject oriented databases designed to support Decision Support Systems (DSS) functions, where each series of data is precise to some period of time. It is said that data warehouse contains atomic data and lightly conclude the data. On the other hand, data mining is the search for valuable information in large volumes of data (Weiss Indurkhya, 1998). It is the process of nontrivial extraction of implicit, previously unknown and potentially useful information such as knowledge rules, constraints, and regularities from data stored in repositories using pattern recognition technologies as well as statistical and mathematical techniques (Technology Forecast, 1997; Piatetsky-Shapiro and Frawley, 1991). As mentioned earlier, many organisations nowadays use computers especially through the usage of information system to collect particulars of business transactions such as records of banking operations, sales of retails, productions of factory, telecommunications and other transactions. Consequently the data mining tools are used to expose positive potentials and association from the data collected. Background of data warehousing and data mining The following part point up the historical evolution of the database and directly discuss about data warehouse and data mining. A brief history of data warehousing and data mining are included. Furthermore is the issues faced in the early years of implementing the concept of data warehousing and data mining and where both concepts are useful. Data warehousing started in the late 1980s from the IBM lab and the responsible researchers are Barry Devlin and Paul Murphy. They started by the development of business data warehouse for decision support surroundings. In the early 1990s, it became a trend for organisations to meet the growing demand for organising information. However Haisten (1999), a columnist for Information Management Website, mentioned that the concept of data warehouse take shape in early 1970s through a study that started out at MIT with the aim to provide optimal technical architecture. And now, the next generation of data warehousing called Trend in Data Warehouse (TDWI) is mushrooming and become popular in many organisations that use information as their vital capitals. The emergence of data mining began in the late of 1980s and it flourished by 1990s. There are three roots that can be traced back along three family lines on the origin of data mining, which are the classical statistics, artificial intelligence, and machine learning. In order to automate the process of extracting the data which are increased every single time, human has increased the power of computer and data storage. For that reason, the amount of data becomes huge and more complex. Primarily, Bayes theorem (1997) and Regression analysis has identify patterns in data. The data mining is actually the process or method by using greater discovering in computer science engineering such as neural networks, clustering process, genetic algorithm and decision trees. Data mining can be said as a method to help with the collection of observation of behaviour. Ayre (2006) stated in his paper that todays data mining techniques is due to the work of mathematician, logicians, and computer scientist join together to create Artificial Intelligence (AI) and Machine Learning dated back from the 1950s. That was a very basic spark for data mining ideology. As mention earlier, in the 1960s, AI and statistic practitioners created new algorithm such as regression analysis, maximum likelihood estimates, neural networks, bias reduction, and linear model. Also in 1960s, the field of information retrieval (IR) made its contribution in the form of clustering techniques and similarity measures. At these time techniques were applied to text document, but they would later be utilized when mining data in databases and other large, distributed data sets (Dunham, 2003). In 1997, Connecticut-based Gartner Group report has mentioned about data mining and artificial intelligence are at the top five ranking of major technology areas that will clearly have a main crash transversely the whole scope of business unit within the incoming three to five years. Presently, data mining techniques and tools are being prolonged to the variety of areas. For instance, the data mining tools like intelligent text-mining system will extract the text waste pertinent to user queries. The above is the process of how the data is transport to database and data warehouse and selection process by using data mining techniques and technology. And then it show us how the information form by the translating the data to be deploy in business. Approaches of data warehousing and data mining in various industries The industry of finance, sales and marketing, administration and others should see information as corporate source but the many local narrow systems that held that information simply did not give way the incorporated commercial viewpoint that was required. (Inmon, 2007) Even though operational data is a greater asset to the organisation, it seemed data is usually not making use to its full capable. Therefore, data warehouse basically is to enable users appropriate access to breaking apart and complete view of the organisation, supporting forecasting and decision-making process at the managerial stage. Additionally, data warehouse can achieve information consistency by carry data from dissimilar data foundations into centre of database. Users from different department for instances, can view the data from consistent single one place repository. The layer of data in data warehouse makes the information consistent by enable data around the data warehouse to be describe in business terms as against to using database terminology. The establishment of data that enforce how business terms are declared or calculated are also defined in the metadata layer and then served to the users. Because of the data in the data warehouse is non-volatile but it must be d esign to adapt the changes periodically. It is because terminologies use in business cannot run from changes. Mannino and Walter (2004) in their study about the refreshment of data warehouse stated that data warehouse refreshment is a complex process comprising many tasks, such as extraction, transformation, integration, cleaning, key management, history management, and loading. This study is base on interviewed of 13 organisations and the author conclude that daily refresh during nonbusiness hours were the most common policy. Sometimes data warehouse is not fully utilized by organisation or it being used by company but not all departments. In a case studied by Payton (2005) conclude that there are three factors why data warehouse is disappointed them. It is because; marketings lack of trust in the data in CDW (Corporate data warehouse); marketings low perceived quality of the data; and marketings perceived lack of incorporation of their needs in the design of the data warehouse and data warehouse interface. Data mining in the industries like information provider as library involved in digital libraries gain benefits from it as they found the method to classify information automatically and apply new way to clustering the subject called MetaCombined the project. Besides database, data mining can be useful in a variety data types like text, spatial data, temporal data, images, and other complex data. Data warehousing and data mining in telecommunication The telecommunication industry is fast fitting the main user of high quantity information system. The problem faced by telecommunication industry is the generation of information which is too fast and in tremendous condition. The difficulties occur when a user, either a manager or high executive, needs access to stored information. If the time is not the issue to search what they want in that kind of stored data where they put in different places, it will not be an issue at all but time limitation is consuming. For instance, in order to produce a report regarding subscriber, an executive need to extract the data, do some analysis, and some other step to make it presentable to their officer. What else can enhance all this besides technology? The exact question to ask is; what is the technology that can be very helpful in this situation? The answer is through the application of data warehousing and data mining. In real case studied by Papaiacovous, Bramblet, and Burgess (n.d) in a paper titled Data Warehouse: A telecommunication Business Solution; they described about the difficulties to produce report. They then design personalized systems which exceed the traditional borders of data warehousing systems by assembling and keeping only important data, analyzing and transforming the data, and then summarizing and rearranging it in according to the demands of the user. Another interesting article by Gomez (1998), expressed the hope that cellular companies and other communications firms to strongly consider data warehousing as a way to achieve competitive advantage. The author also reviews new way to data warehousing that have established successful in compliant concrete business benefits. Service providers realize due to the competition in the marketplace, they need to provide the best for their customer or risk to lose them. It is because customer can simply change their telecommunication service provider if they are not satisfied with their current provider. So the provider must get the knowledge in customers hand about what they want actually. After all the data about the customer are collected via online and phone survey, a data warehouse can enhance the executive to analyze and segments customer into groups by their product usage patterns, demographic characteristics, etc. Telecommunications companies produce tremendous quantity of data. These data consist of call detail data, which describes the calls that cross the telecommunication networks; network data, which explain the position of the hardware and software components in the network, and customer data. Data mining can be used to uncover useful information buried within these data sets. Telecommunication companies might counter fraud from customer that intends to use the service without paying for it. It happens when the users register and manipulate the registration information. The most regular way for identifying fraud is to construct a profile of customers calling behaviour and compare recent activity against this behaviour. Thus, this data mining application relies on deviation detection. The calling behaviour is captured by summarizing the call detail records for a customer. Here is the issue on data mining. In the customer case study by the company ECtel n order to sell their data mining product for fraud detection called FraudView noted that selling data mining product to a telecommunication provider has been traditionally difficult because they dont have data mining experts on staff who can work conventional data mining tools. Additionally, there are many ways to run away from paying for telecommunication services, from stealing phone card to bypassing phone circuitry. ECtel created FraudView, the solution that uses SPSS Inc.s advanced data mining workbench, which enable the detection of telecommunications fraud in real time. Data mining in telecommunication industries is not limited to detect fraud only but it also can be used as network fault isolation, marketing or customer profiling, etc. This is owing to the three main sources of telecommunication data which are call detail, network, and customer data. Data warehouse and data mining in financial services How a retail bank can truly understand and predict its customers needs to the point where it can design product and services that suit those needs? One way of looking at customers can be from the standpoint of channel usage. In the UKs Llyods Bank/TSB merger, data were sourced from both their data warehouse, and then used to segment the customer base by service channel usage. Customers were allocated to segment on their usage of the following channels: ATMs, automated (direct debits/standing orders), cards (credit card and debit) and telephone (Peppard, 2000). Financial institutions struggle with the large amount of data on every transaction deal. Data warehouse helps financial service organisations to analyse large, complex, and rapidly growing data volumes in a quicker way for better decision making and faster speed back to the market. Fundamentals of data mining in finance are coming from the need to forecast multidimensional time series with high level of noise, accommodate specific efficiency criteria, make coordinated multiresolution forecast, and also incorporate a stream of text signals as input data for forecasting models (Kovalerchuck Vityaev, 2002 ). As noted by Kovalerchuck Vitayaev, four main reason why data mining need to be implemented in finance is because the emergence of high volume databases such as commercial data warehouse and computer automated data recording; advances in computer technology such as faster and bigger computer engines and parallel architectures; fast access to vast amounts of data, and the ability to apply computationally intensive statistically methodology to these data. Data mining is used to forecast the target variable, performing the contribution varies in percent within todays closing price and the price five days later, along with next days prediction. Data warehouse and data mining in health service In healthcare there is not much transaction as business environment. The data is about outpatient, visits to doctor office, procedure and so forth. Instead of numerical data, healthcare has textual description if the different medical counters. And there is a little bit problems here, where the technology that own a old method of data warehouse is created to manage process of transacting data that is very conquered by arithmetical information. When textual, non-transactional information is come across, the old method data warehouse technology nowadays is simply at a defeat to handle healthcare information. (Inmon, 2007). Then, if the data is not a number but a textual; it must be kept with different understanding of phrase. It just likes a different language. In order to be standardized, there has to be creation of same vocabulary for instance, with the purpose to gain understanding for all. Then it can be kept in the data warehouse. In a case study written by Kumar and Raval (n.d), they traced a large global pharmaceutical, which has a huge data of clinical trials for a number of drugs projects. Due to data collection and analyses operations that are broadening across the world, it is harder to implement data standards. Even harder to enforce was the programming and validation standards that are required of pharmaceutical companies. Primarily, a data warehouse is an operational middle ground and disparate and incompatible to a big quantity of systems put together to diverse collection from end user platform. In another case, Whiting (2001) reported a healthcare name Intermountain Health that used data warehouse to make an analysis handling provided to its cardiovascular patients for five years. From the result, it improves service provided after the patients return home. These are the data mining in healthcare and insurance where it can give beneficial such as providing claims analysis, it means determine which medical procedure are claimed together. It helps in predicting which customer will buy new policies and can identify behaviour pattern or risky customer and also prevent fraud. Data warehouse and data mining in retail industry The challenge in retailer business actually is inundate of data, the battle of data and expired data. To cope with these challenges, many retailers are building unified repositories of data known as data warehouse. In the early implementation of data warehousing technology in 1990s, the retail business has gained benefits of practical data warehouse. From the daily historical sales reporting database created over past few years ago, retailer can expanded the use of analytical systems to support and produce vital decision. The retail industry is going through a transformation. Data warehouse enable retailers to carry out on their major products, including activities such as inventory replacement, purchasing, and vendor management across multiple other multiple. Financial planning, adjusting for stock outs to seed a top-down financial plan provides all of the data necessary to support well-organized process for the confirmation of invoice accuracy to strategy-based pricing solution. Simple application that can implement the concept of data mining for retail industries are SQL server 2008 and Microsoft Office Excel 2007. To stay competitive, retailer must understand not only current consumer behaviour but must also be able to predict future consumer behaviour. Accurate prediction and an understanding of customer behaviour can help retailers keep customers, improve sales, and extend the relationship with their customers. SQL server 2008 provide predictive analysis through data mining and Microsoft Excel 2007 offer data mining capabilities that can help retailers make better decision. The application that is common for business retail in data mining such as market basket analysis, fraud detection, database marketing, sales forecasting, and also merchandise planning and allocation. Data mining is so beneficial in retailer industries! Recommendations In the business world a transaction is repeated again and again and many of them deal with data in numerical. The same activity repeats with different customers and different figures. To release from this mess, data warehouse and data mining provide solution. Even though data warehouse and data mining is a strategic investment to the business world but it can be risky without a proper understanding of the concept. Governance or control is important to support the implementation of data warehouse and data mining. There must be a proper standard to ensure compatibility in processing the data especially for textual data used in the health industry. There should also be a policy and to manage the data warehouse. It is highly recommended that to be successful in the implementation of data warehouse or/and data mining, an organisations are required to have extensive or comprehensive knowledge about the data in their company. This is to guarantee that a well structured data warehouse can be constructed. A well structured data warehouse consequently will help organisation to exploit via data mining the data that they have. Organisation should also know what exactly they want to implement in their organisation so that the right tools for data mining can be used. And finally, a strong support from top management is important to deploy data warehouse and data mining because the investment on these is not cheap. Conclusion Insufficient of data is no longer a trouble but lack of ability to breed valuable information from data is the issue today. The answer for those issues is through the implementation of data warehouse and the power to use data mining techniques and tools. Nevertheless, the realisation and the awareness of data warehouse and data mining in the organisation should take into consideration many aspects regardless of what industries. The aspects include support of the top management, understanding of the data needed by the organisation, governance and policy, the right design of the data warehouse, and the right tools or techniques for data mining. Bibliography Dunham, M.H. (2003). Data mining introductory and advanced topics. Upper Saddle River, NJ: Pearson Education, Inc. Kovalerchuk, B., Vityaec, E. (2002). Data mining in finance advances in relational hybrid methods. USA: Kluwer Academic Publisher. Wang, J. (2003). Data mining opportunities and challenges. USA : Idea Group Publishing. Keng Siau. (2003). Advanced Topics in database research. USA : Idea Group Publishing. M. Kumar Sagar., Raval, H. (n.d). Data warehousing in pharmaceutical and healthcare: an industry perspective. 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War in Iraq: There Was Another Option Essay -- Politics Political

War in Iraq: There Was Another Option 1. As our brave men and women in uniform find themselves embroiled again in a conflict in the Middle East, debate surrounding the timeliness and necessity of this second Gulf conflict has ceased in most professional circles. However, before the current conflict began, controversy raged over when and how to best prosecute this situation. Many argued that the United States should have worked through the United Nations to pursue a resolution that had the consensus of the world behind it. That endeavor, however, was doomed to failure from the start. The United States sought to solve this dilemma using military force. France and Germany desired to diffuse it using anything but force. In order to properly evaluate all options in this case, one must ask themselves how immediate a threat did Saddam Hussein pose to the United States and what is the best way to counter that threat? In my estimation, Saddam Hussein poses no immediate threat to the United States. Thus, the US government had the time to cultivate a plan to remove him from power that was agreeable to all involved. 2. One must examine the fine points of each argument that the administration had proposed for the immediacy of war in order to best refute them. The first and often most repeated argument that Saddam Hussein posed a direct threat to the United States is that he possessed weapons of mass destruction. I am willing to concede that the Iraqi military possessed both chemical and biological agents. Saddam Hussein had stockpiles of them that were never accounted for after the first Gulf War. He has even used them on several occasions on Kurdish minorities in Northern Iraq. However, no proof was ever offered that he possessed nu... ...o.com/news?tmpl=story&u=/ ap/20030411/ap_on_re_mi_ea/war_us_military_623> McDowell, Patrick. Crowd Kills 2 Clerics at Shiite Shrine . Middle East AP. April 11 th 2003. < http://story.news.yahoo.com/news?tmpl=story&u=/ ap/20030411/ap_on_re_mi_ea/war_clerics_killed&cid=540&ncid=716> Mearsheimer, John J. and Stephen M. Walt. An Unnecessary War . Foreign Policy. January/February 2003. 45-48 Pollack, Kenneth M. The Threatening Storm: The Case for Invading Iraq . Random House Publishing. First Edition 2002. 64, 144 Rai, Milan. No Justification For War . Znet. March 6 2002. Community for Social Change. April 13 2003. Rice, Condoleezza. Campaign 2000: Promoting the National Interest . Foreign Affairs. January/February 2000. 86 Tenet, George. Letter to Congress . October 2002

Essay About an Injury

My Injury Name: Pleun Fijneman Class: 2H Teacher: Nugteren Name: Pleun Fijneman Class: 2H Teacher: Nugteren What happened? I’m going to tell you about the fracture me brother got. It happened one day before we went on wintersports. He was playing soccer with his friends, and one of them thought it was funny to make him stumble, so he went leg hooking at my brother. My brother felt and he had a lot of pain in his leg. He couldn’t walk on it anymore, so they all helped my brother to the house of one of them. The mother of the friend where they were, brought my brother to our house.My brother looked very pale. Then my mother brought him to the emergency room, and he was sent to the hospital. When they arrived at the hospital they had to wait for a long time. He said to me he had never felt so much pain as then. Then a doctor came and took him to the plastery room. First, he was laid down in a chair, and then the doctor tried to put his leg in the right position. My brother was dying of pain when the doctor touched it, and certainly when he was putting his leg in right position. After that the doctor made a X-ray of his leg, so he could see what was wrong.The doctor told my mom that he had a crack in his tibia (shin bone). The doctor was telling about fractures, but my mom didn’t know what it meant, so the doctor explained the meaning: â€Å"it’s a crack, break, or shattering of a bone, and Toots (my brother) has a crack in his bone. He has a tibia stress fracture. It’s an acute traumatic injury. which usually involve a single blow from a single application of force. † The joints which were involved: knee joint, ankle joints (and hinge joints). He couldn’t move them for 6 weeks, because the plaster was wrapped around it. RecoveryDuring recovering my brother did go with us on wintersports, but he said it was very boring for him. Because he did not go skiing or snowboarding. He had to stay at the house which we had hir ed, or go with us to the snow mountain range where we would go skiing. Most times he stayed home, because it was not very handy to always bring a wheelchair. When we got back home, the friend who made him stumble came over to apologize. Now it happened 4 years ago, but my brother is still a little bit mad at him. Because it was our first wintersports vacation and he couldn’t go skiing.When school started, he stayed most of the time at home, or he came to school at middays. He was at home for 6 weeks. After 3 weeks, he got new plaster. The plaster helped to keep the bone in the same place, so it could cure well. after 2 weeks with the new plaster he got walking cast and crutches, so he could go back to school again and learn how to walk. When he was at school, all his friends started writing sweet things on his leg. My brother was very happy that he could go to school again. Then finally his plaster could go off. His leg was very thin, and it looked a little bit scary, but aft er one week it looked normal again. Photo

Marketing Analysis General Mills - 989 Words

General Mills is a company that has strategically developed and growth through mergers and acquisitions. Mergers are the fusion of two companies that join forces to compete in the market. There are two types of merger: Horizontal merger on which the company acquires a competitor and vertical merger, on which the fusion is with a supplier. Acquisitions, on the other hand occurs when a company buys another company and become the property of the buyer. Thorough study of the market has made General Mills maintains a leader position on the food industry through more than 100 years in the market. According to a business encyclopedia, Strategy is a plan a company develops to reach a determine objective and reflects the company’s strength,†¦show more content†¦Furthermore, a good marketing strategy will tell you how to advertise your product, your target audience and will add value to the product. 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With the large salesforce that GMI has, it was able to reassign a lot of ColomboRead MoreMarketing Plan For Quality Freelance Writing Company Essay1480 Words   |  6 Pages GreaterThings Freelance Content Company Marketing Plan Quality Freelance Writing Companyâ€Æ' Table of Contents Topic Page # Company Description 3 Business Mission 4 Situation Analysis (SWOT Analysis) 5 Objectives 10 Marketing Strategy 12 Implementation, Evaluation and Control 21 Summary 24 Works Cited 25 Company Description GreaterThings Freelance Content Company (GreaterThings FCC) is a writing company thatRead MoreCase Study Steelco1314 Words   |  6 Pages| Steelco | | | Week 3 | Case Analysis of Marketing | | | Steelco Case Analysis of Marketing Introduction The so-called I-beams are a standard element in modern construction used to build e.g. bridges, stadiums and super high-rise buildings. The I-beam market can be further segmented into small size beams up to 14-inches, in which a number of firms are active and a kind of perfect competition is taking place. As for the 14-inch to 24-inch range only Steelco and USX remain in anRead MoreCase Study Steelco1301 Words   |  6 Pages| Steelco | | | Week 3 | Case Analysis of Marketing | | | Steelco Case Analysis of Marketing Introduction The so-called I-beams are a standard element in modern construction used to build e.g. bridges, stadiums and super high-rise buildings. The I-beam market can be further segmented into small size beams up to 14-inches, in which a number of firms are active and a kind of perfect competition is taking place. As for the 14-inch to 24-inch range only Steelco and USX remain in an oligopolyRead MoreThe Progress Of Allstar Through The Last Ten Periods762 Words   |  4 PagesThe purpose of this document is to discuss the progress of Allstar through the last ten periods. Analyze the marketing strategy and rational for the decisions and adjustments in each period and highlight the lessons learnt from the experience. The high level objectives set for Allstar at the beginning of the period were as follows: †¢ Grow the revenue of Allstar from $355 Mill in the short-term by 15.8%, the industry growth rate is average 10.3% †¢ Increase the current gross margin of 48% to an industryRead MoreCpw and Kelloggs6179 Words   |  25 PagesRunning head: CEREAL PARTNERS WORLDWIDE (CPW) CASE ANALYSIS 1 Cereal Partners Worldwide (CPW): The No. 2 world player is challenging the No. 1 – Kellogg International Marketing – Assignment 1 Candidate: Emad AbouElgheit ISM - International School of Management Doctor of Philosophy (Ph.D.) Presented to: Professor Peter Horn 21 November 2011 Word Count: 4,326 CASE ANALYSIS - CEREAL PARTNERS WORLDWIDE (CPW) 2 Abstract The paper analyzes the case study developed in 2007 of Cereal PartnersRead MoreOlive Garden Essay1561 Words   |  7 PagesOlive Garden Project Marketing Plan: Phase I During week two, Learning Team B will take a thorough look at the Olive Garden Italian Restaurant chain. Team B has decided that a new appetizer item should be added to the restaurant menu. The appetizer item being considered is cheese filled breadsticks served with Marinara sauce. The team will begin this marketing plan by giving an overview of the Olive Garden Restaurant, along with a detailed description of the new menu item being consideredRead MoreThe Total Global Retail Value For Yogurt1026 Words   |  5 Pagesin 2010. Companies that dominate the U.S yogurt market currently are Danone, Chobani LLC and General Mills. Yoplait by General Mills who used to be the country’s leading brand, has been surpassed in the US yogurt market by privately-owned Greek yogurt maker Chobani. While Dannon represents the top yogurt brand in the US with a 34% market share (Shoup 2017). Yoplait is originally from France and General Mills held the US license for Yoplait since the 1970s and it acquired a 51% stake in the brand in