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978-3-319-05461-2 |
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140320s2014 sz | s |||| 0|eng d |
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|a 9783319054612
|9 978-3-319-05461-2
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|a 10.1007/978-3-319-05461-2
|2 doi
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|a Business Intelligence
|h [electronic resource] :
|b Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial Lectures /
|c edited by Esteban Zimányi.
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|a 1st ed. 2014.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2014.
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|a IX, 243 p. 95 illus.
|b online resource.
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|a text
|b txt
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|a Lecture Notes in Business Information Processing,
|x 1865-1356 ;
|v 172
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|a Introduction 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.
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|a To 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.
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|a Business information services.
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|a Data mining.
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|a Information storage and retrieval systems.
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|a Information technology
|x Management.
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|a Computer science
|x Mathematics.
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|a Mathematical statistics.
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|a IT in Business.
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|a Data Mining and Knowledge Discovery.
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|a Information Storage and Retrieval.
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|a Computer Application in Administrative Data Processing.
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650 |
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|a Probability and Statistics in Computer Science.
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700 |
1 |
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|a Zimányi, Esteban.
|e editor.
|0 (orcid)0000-0003-1843-5099
|1 https://orcid.org/0000-0003-1843-5099
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9783319054629
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776 |
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|i Printed edition:
|z 9783319054605
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776 |
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|i Printed edition:
|z 9783030396718
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830 |
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|a Lecture Notes in Business Information Processing,
|x 1865-1356 ;
|v 172
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|u https://doi.uam.elogim.com/10.1007/978-3-319-05461-2
|z Texto Completo
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|a ZDB-2-SXCS
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|a Computer Science (SpringerNature-11645)
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950 |
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|a Computer Science (R0) (SpringerNature-43710)
|