Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
Concise and comprehensive introduction to business intelligence (BI)
Combines traditional BI technologies with new topics such as business semantics, Big Data analysis, and multicriteria decision making
Includes recent developments in underlying basic technologies such as machine learning, logic networks, and graph mining
Contributions conjointly written by leading academic researchers and industrial developers, striving for both high relevance and real-world applicability
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.
Content Level »Research
Keywords »BPEL - BPMN - Bayesian Networks - Big Data - Business Intelligence - Business Process Management - Business Semantics - Data Warehouses - Graph Mining - Machine Learning - MapReduce - Markov Logic Networks - Multicriteria Decision Making - OLAP - Online Analytical Processing - Ontologies
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.