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020 _a9783319054612
_9978-3-319-05461-2
024 7 _a10.1007/978-3-319-05461-2
_2doi
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072 7 _aKJQ
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072 7 _aBUS083000
_2bisacsh
072 7 _aCOM039000
_2bisacsh
082 0 4 _a650
_223
100 1 _aZimányi, Esteban.
_eeditor.
245 1 0 _aBusiness Intelligence
_h[electronic resource] :
_bThird European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial Lectures /
_cedited by Esteban Zimányi.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aIX, 243 p. 95 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Business Information Processing,
_x1865-1348 ;
_v172
505 0 _aIntroduction to Pattern Mining -- Process Mining in the Large: A Tutorial -- Ontology-Driven Business Intelligence for Comparative Data Analysis -- Open Access Semantic Aware Business Intelligence -- Transparent Forecasting Strategies in Database Management Systems -- On Index Structures for Star Query Processing in Data Warehouses -- Intelligent Wizard for Human Language Interaction in Business Intelligence.
520 _aTo large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the “Big Data” phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data. The lectures held at the Third European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., pattern and process mining, business semantics, Linked Open Data, and large-scale data management and analysis. Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.
650 0 _aEconomics.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aInformation systems.
650 0 _aManagement information systems.
650 1 4 _aEconomics/Management Science.
650 2 4 _aBusiness Information Systems.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aComputer Appl. in Administrative Data Processing.
650 2 4 _aProbability and Statistics in Computer Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319054605
830 0 _aLecture Notes in Business Information Processing,
_x1865-1348 ;
_v172
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-05461-2
912 _aZDB-2-SCS
999 _c18420
_d18420