Business Intelligence [electronic resource] : Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures /
European Business Intelligence Summer School(2nd :2012 :Brussels, Belgium)
Aufaure, Marie-Aude.editor.
Zimányi, Esteban.editor.(orcid)0000-0003-1843-5099https://orcid.org/0000-0003-1843-5099edt
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 Second 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., machine learning, logic networks, graph mining, business semantics, large-scale data management and analysis, and multicriteria and collaborative decision making. 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.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Managing Complex Multidimensional Data -- An Introduction to Business Process Modeling -- Machine Learning Strategies for Time Series Forecasting -- Knowledge Discovery from Constrained Relational Data: A Tutorial on Markov Logic Networks -- Large Graph Mining: Recent Developments, Challenges and Potential Solutions -- Big Data Analytics on Modern Hardware Architectures: A Technology Survey -- An Introduction to Multicriteria Decision Aid: The PROMETHEE and GAIA Methods -- Knowledge Harvesting for Business Intelligence -- Business Semantics as an Interface between Enterprise Information Management and the Web of Data: A Case Study in the Flemish Public Administration.
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 Second 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., machine learning, logic networks, graph mining, business semantics, large-scale data management and analysis, and multicriteria and collaborative decision making. 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.
Information technology.
Business-Data processing.
Application software.
Database management.
Information storage and retrieval.
Computer science-Mathematics.
Mathematical statistics.
IT in Business.
Computer Appl. in Administrative Data Processing.
Database Management.
Information Storage and Retrieval.
Discrete Mathematics in Computer Science.
Probability and Statistics in Computer Science.