Data Mining

Powerful Essays
Topics: Data mining
Question 1: Case One –eBay Q1.1. Discuss the relationships between business intelligence, data warehouse, data mining, text and web mining, and knowledge management. Justify and synthesis your answers/viewpoints with examples (e.g. eBay case) and findings from literature/articles. To understand the relationships between these terms, definition of each term should be illustrated. Firstly, business intelligence (BI) in most resource has been defined as a broad term that combines many tools and technologies, used to extract useful meaning of enterprise data in order to help the decision maker. Turban, Sharda, Aronson, and King (2008) said: ‘Business Intelligence is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. It is a content-free expression, so it means different things to different people. BI’s major objective is to enable interactive access (sometimes in real time) to data, to enable manipulation of data, and to give business managers and analysts the ability to conduct appropriate analysis. By analysing historical and current data, situations, and performances, decision makers acquire valuable insights that enable them to make more informed, timely, and consequently better decision’ p.28. The EBSP glossary defines business intelligence as ‘a broad term for software reporting tools that pull data from various sources to generate customizable reports’ (EBSP, 2009). Rud (2009) also defined BI as a set of architectures, methodologies, theories, processes and technologies that aims to deliver meaningful and useful information for business purposes. He argues that BI is a gate of new opportunities that bring a business into a competitive market advantage and ensures long-term stability. However, from the above definitions it is clear that BI is capable of providing a holistic view of the business by utilizing organisation resources such as database (i.e., data warehouse and data mart) and data


References: Bali, S. (2012). IT vendors must explore “analytics as a service” for telcos [online]. OVUM. Available at :http://ovum.com/2012/01/30/it-vendors-must-explore-analytics-as-a-servicefor-telcos/ [Accessed 5 April 2013]. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Wadsworth & Brooks. Cole Pacific Grove, CA. CompuBase. (n.d). IT & Telecom Distribution Glossary [online]. Available at: http://en.compubase.net/glossary/IT-Telecom-Distribution-Glossary_gi2586.html?l=S [Accessed 2 April 2013]. Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: the new science of winning. Harvard Business School Press. Davis, R. H., Edelman, D. B., & Gammerman, A. J. (1992). Machine-learning algorithms for credit-card applications. IMA Journal of Management Mathematics, 4(1), 43-51. Delen, D., & Crossland, M. D. (2008). Seeding the survey and analysis of research literature with text mining. Expert Systems with Applications, 34(3), 1707-1720. Desai, V. S., Crook, J. N., & Overstreet Jr, G. A. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95(1), 24-37. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37. Friedman, J. H. (1991). Multivariate adaptive regression splines. The annals of statistics, 167. Goul, M. (2010). eBay Analytics 2010: Innovation inspired by Opportunity. BI Congress II. Harris, D. (2011). Six companies doing big data in the cloud [online]. GIGAOM. Available at: http://gigaom.com/2011/09/06/6-companies-doing-big-data-in-the-cloud/ [Accessed 7 April 2013]. Henley, W. E. (1995). Statistical aspects of credit scoring. Dissertation, The Open University, Milton Keynes, UK. 19 Henley, W. E., & Hand, D. J. (1996). A k-nearest-neighbour classifier for assessing consumer credit risk. The Statistician, 77-95. Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F. B., & Babu, S. (2011, January). Starfish: A self-tuning system for big data analytics. In Proc. of the Fifth CIDR Conf. Herschel, R. T., & Jones, N. E. (2005). Knowledge management and business intelligence: the importance of integration. Journal of Knowledge Management, 9(4), 45-55. Herschel, R., & Yermish, I. (2009). Knowledge management in business intelligence. In Knowledge management and organizational learning (pp. 131-143). Springer US Huang, C.-L., Chen, M.-C., & Wang, C.-J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847– 856. doi:10.1016/j.eswa.2006.07.007. Jackson, J. (2002). DATA MINING : A CONCEPTUAL OVERVIEW, 8, 267–296. Krishna, P. R., & Varma, K. I. (n.d.). Cloud Analytics A Path Towards Next Generation Affordable BI. Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50(4), 1113-1130. Personal, M., & Archive, R. (2008). Credit scoring with boosted decision trees, (8156). Proffitt, B. (2012). What AaaS needs to really succeed.BigQuery is latest analytics as a service, but is it a complete solution? [online]. Available at: http://www.itworld.com/bigdatahadoop/273952/what-aaas-needs-really-succeed [Accessed 10 April 2013]. Rud, O. P. (2009). Business intelligence success factors: tools for aligning your business in the global economy (Vol. 18). Wiley. Shmueli, G., Patel, N. R., & Bruce, P. C. (2011). Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. Wiley. Tang, H., Yang, Z., Zhang, P., & Yan, H. (2008, December). Using Data Mining to Accelerate Cross-Selling. In Business and Information Management, 2008. ISBIM '08. International Seminar on (Vol. 1, pp. 283-286). IEEE. 20 Turban, E., Sharda, R., Aronson, J. E., & King, D. N. (2008). Business intelligence: a managerial approach. Upper Saddle River: Pearson Prentice Hall. West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27(11), 1131-1152. 21

You May Also Find These Documents Helpful

  • Satisfactory Essays

    Data Mining

    • 2278 Words
    • 10 Pages

    Stock Exchange forecasting with Data Mining and Text Mining (Marketing and Sales Analysis) Full names : Fahed Yoseph TITLE : Senior software and Database Consultatnt (Founder of Info Technology System) E-mail: Yoseph@info-technology.net Date of submission: Sep 15th of 2013 CONTENTS PAGE Chapter 1 1. ABSTRACT 2 2. INTRODUCTION 3 2.1 The research problem. 4 2.2 The objectives of the proposal. 4 2.3 The Stock Market movement. 5 2.4 Research question(s). 6 2. Background 3. Problem…

    • 2278 Words
    • 10 Pages
    Satisfactory Essays
  • Powerful Essays

    Data Mining

    • 1453 Words
    • 6 Pages

    SMS CUSAT Reading Material on Data Mining Anas AP & Alex Titty John • What is Data? Data is a collection of facts and information or unprocessed information. Example: Student names, Addresses, Phone Numbers etc. • What is a Database? A structured set of data held in a computer which is accessible in various ways. Example: Electronic Address Book, Phone Book. • What is a Data Warehouse? The electronic storage of large amount of data by business. Concept originated in…

    • 1453 Words
    • 6 Pages
    Powerful Essays
  • Powerful Essays

    Data Mining

    • 2070 Words
    • 9 Pages

    Data Mining Melody McIntosh Dr. Janet Durgin Information Systems for Decision Making December 8, 2013 Introduction Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge- driven decisions Although data mining is still in its infancy…

    • 2070 Words
    • 9 Pages
    Powerful Essays
  • Better Essays

    Data Mining

    • 1710 Words
    • 7 Pages

    Data Mining Assignment 4 Shauna N. Hines Dr. Progress Mtshali Info Syst Decision-Making December 7, 2012 Benefits of Data Mining Data mining is defined as “a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban & Volonino, 2011). The information identified using data mining includes patterns indicating trends, correlations…

    • 1710 Words
    • 7 Pages
    Better Essays
  • Better Essays

    Data Mining

    • 2354 Words
    • 10 Pages

    Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business 's product or also in winning additional customers that may be purchasing from the competitor. Generally, data are any “facts, numbers, or text that can be processed by a computer.” Today…

    • 2354 Words
    • 10 Pages
    Better Essays
  • Satisfactory Essays

    Data mining

    • 1174 Words
    • 5 Pages

    University CS 450 Data Mining, Fall 2014 Take-Home Test N#1 Date: September 22nd, 2014 Final deadline for submission September 29th, 2014 Weighting: 5% Total number of points: 100 Instructions: 1. Attempt all questions. 2. This is an individual test. No collaboration is permitted for assessment items. All submitted materials must be a result of your own work. Part I Question 1 [20 points] Discuss whether or not each of the following activities is a data mining task. •…

    • 1174 Words
    • 5 Pages
    Satisfactory Essays
  • Good Essays

    Data Mining

    • 350 Words
    • 2 Pages

    a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer. Data Mining is generally used for four main tasks: (1) to improve the process of making new customers and retaining customers; (2)…

    • 350 Words
    • 2 Pages
    Good Essays
  • Powerful Essays

    Data Mining

    • 1921 Words
    • 8 Pages

    Data Mining Information Systems for Decision Making 10 December 2013 Abstract Data mining the next big thing in technology, if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining…

    • 1921 Words
    • 8 Pages
    Powerful Essays
  • Powerful Essays

    Data Mining

    • 3792 Words
    • 16 Pages

    university CASE STUDY OF DATA MINING Summitted by Jatin Sharma Roll no -32. Reg. no 10802192 A case study in Data Warehousing and Data mining Using the SAS System. Data Warehouses The drop in price of data storage has given companies willing to make the investment a tremendous resource: Data about their customers…

    • 3792 Words
    • 16 Pages
    Powerful Essays
  • Good Essays

    Data Mining

    • 782 Words
    • 4 Pages

    Data Mining Abdullah Alshawdhabi Coleman University Simply stated data mining refers to extracting or mining knowledge from large amounts of it. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, data mining should have been more appropriately named “knowledge mining from data,” which is unfortunately somewhat long. Knowledge mining, a shorter term, may not…

    • 782 Words
    • 4 Pages
    Good Essays