000 04354nam a22005535i 4500
001 978-3-662-46531-8
003 DE-He213
005 20200421111853.0
007 cr nn 008mamaa
008 150602s2015 gw | s |||| 0|eng d
020 _a9783662465318
_9978-3-662-46531-8
024 7 _a10.1007/978-3-662-46531-8
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aGrossmann, Wilfried.
_eauthor.
245 1 0 _aFundamentals of Business Intelligence
_h[electronic resource] /
_cby Wilfried Grossmann, Stefanie Rinderle-Ma.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2015.
300 _aXVIII, 348 p. 116 illus., 81 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aData-Centric Systems and Applications,
_x2197-9723
505 0 _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.
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.
650 0 _aComputer science.
650 0 _aManagement information systems.
650 0 _aIndustrial management.
650 0 _aData mining.
650 0 _aApplication software.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aBusiness Information Systems.
650 2 4 _aComputer Appl. in Administrative Data Processing.
650 2 4 _aInformation Systems Applications (incl. Internet).
650 2 4 _aBusiness Process Management.
700 1 _aRinderle-Ma, Stefanie.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783662465301
830 0 _aData-Centric Systems and Applications,
_x2197-9723
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-662-46531-8
912 _aZDB-2-SCS
942 _cEBK
999 _c56215
_d56215