Data & Knowledge Engineering Introduction Database Systems and Knowledgebase Systems share many common principles. Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKEreaches a world-wide audience of researchers‚ designers‚ managers and users. The major aim of the journal is to identify‚ investigate and analyze the underlying principles in the design and effective use of these systems.DKE achieves this aim
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CHAPTER 3 DATABASES AND DATA WAREHOUSES Building Business Intelligence CONTACT INFORMATION: Stephen Haag is the primary author of this chapter. If you have any questions or comments‚ please direct them to him at shaag@du.edu. THIS CHAPTER/MODULE IN SHORT FORM… This chapter introduces your students to the vitally important role of information in an organization and the various technology tools (databases‚ DBMSs‚ data warehouses‚ and data-mining tools) that facilitate the management
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provide an opportunity to construct and store the huge amount of data together from many fields such as business‚ administration‚ banking‚ the delivery of social and health services‚ environmental safety‚ security and in politics. Typically‚ these data sets are very huge and regularly growing and contain a huge number of compound features which are hard to manage. Therefore‚ mining or extracting association rules from large amount of data in the database is interested for many industries which can help
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Mining Changes for Real-Life Applications Bing Liu‚ Wynne Hsu‚ Heng-Siew Han and Yiyuan Xia School of Computing National University of Singapore 3 Science Drive 2 Singapore 117543 {liub‚ whsu‚ xiayy}@comp.nus.edu.sg Abstract. Much of the data mining research has been focused on devising techniques to build accurate models and to discover rules from databases. Relatively little attention has been paid to mining changes in databases collected over time. For businesses‚ knowing what is changing and
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http://hdl.handle.net/2451/31553 Data Science and Prediction Vasant Dhar Professor‚ Stern School of Business Director‚ Center for Digital Economy Research March 29‚ 2012 Abstract The use of the term “Data Science” is becoming increasingly common along with “Big Data.” What does Data Science mean? Is there something unique about it? What skills should a “data scientist” possess to be productive in the emerging digital age characterized by a deluge of data? What are the implications for business
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Data warehousing and OLAP Swati Vitkar Research Scholar‚ JJT University‚ Rajasthan. Abstract: Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support‚ which has increasingly become a focus of the database industry. Many commercial products and services are now available‚ and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared
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The Big Data Challenges Lamin A Tunkara CIS 500 – Information Systems for Decision Making Dr. Edwin OTTO July 16‚ 2013 Abstract The paper explores the challenges of big data and the strategies deployed by Volvo Car Corporation in instituting an information technology infrastructural tool that helped the company tap into its massive data‚ bank to extract information for meaningful process improvements. The paper culled information from Dumbill (2012) who in an article
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Introduction Big Data is indeed a better idea. Every day‚ we create 2.5 quintillion bytes of data–so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: posts to social media sites‚ digital pictures and videos posted online‚ transaction records of online purchases‚ and from cell phone GPS signals to name a few. This data is big data. “Much like the scientific principle that we can’t observe a system without changing it‚ big data can’t
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Data Warehousing Failures Eight studies of data warehousing failures are presented. They were written based on interviews with people who were associated with the projects. The extent of the failure varies with the organization‚ but in all cases‚ the project was at least a disappointment. Read the cases and prepare a one or two page discussion of the following: 1. What’s the scope of what can be considered a data warehousing failure? Discuss. 2. What generalizations apply across
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Association Rule Mining using Apriori Algorithm Ghanshyam Verma‚ Shruthi Varadhan Computer Technology Department KITS-Ramtek‚ Nagpur-441106 gs.verma@live.com shruthivaradhan@gmail.com Abstract— Data mining is the process that results in the discovery of new patterns in large data sets. Data mining involves six common classes of tasks: Anomaly detection‚ Association Rule Mining‚ clustering‚ classification‚ regression and summarization. Association rule learning is a popular and well researched
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