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000727650 020__ $$a9783662465318$$qelectronic book
000727650 020__ $$a3662465310$$qelectronic book
000727650 020__ $$z9783662465301
000727650 0247_ $$a10.1007/978-3-662-46531-8$$2doi
000727650 035__ $$aSP(OCoLC)ocn910964163
000727650 035__ $$aSP(OCoLC)910964163
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000727650 050_4 $$aQA76.9.D343$$bG76 2015eb
000727650 08204 $$a006.312$$223
000727650 1001_ $$aGrossmann, Wilfried,$$d1948-$$eauthor.
000727650 24510 $$aFundamentals of business intelligence$$h[electronic resource] /$$cWilfried Grossmann, Stefanie Rinderle-Ma.
000727650 264_1 $$aHeidelberg :$$bSpringer,$$c2015.
000727650 300__ $$a1 online resource (xviii, 348 pages) :$$billustrations.
000727650 336__ $$atext$$btxt$$2rdacontent
000727650 337__ $$acomputer$$bc$$2rdamedia
000727650 338__ $$aonline resource$$bcr$$2rdacarrier
000727650 4901_ $$aData-Centric Systems and Applications,$$x2197-9723
000727650 504__ $$aIncludes bibliographical references.
000727650 5050_ $$a1 Introduction -- 2 Modeling in Business Intelligence -- 3 Data Provisioning -- 4 Data Description and Visualization -- 5 Data Mining for Cross-Sectional Data -- 6 Data Mining for Temporal Data -- 7 Process Analysis -- 8 Analysis of Multiple Business Perspectives -- 9 Summary -- A Survey on Business Intelligence Tools.
000727650 506__ $$aAccess limited to authorized users.
000727650 520__ $$aThis book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques, and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described, and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided. The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.
000727650 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 12, 2015).
000727650 650_0 $$aData mining.
000727650 650_0 $$aManagement information systems.
000727650 650_0 $$aBusiness intelligence.
000727650 7001_ $$aRinderle-Ma, Stefanie,$$eauthor.
000727650 830_0 $$aData-centric systems and applications.
000727650 852__ $$bebk
000727650 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-662-46531-8$$zOnline Access$$91397441.1
000727650 909CO $$ooai:library.usi.edu:727650$$pGLOBAL_SET
000727650 980__ $$aEBOOK
000727650 980__ $$aBIB
000727650 982__ $$aEbook
000727650 983__ $$aOnline
000727650 994__ $$a92$$bISE