Utilizing Big Data Paradigms for Business Intelligence

Utilizing Big Data Paradigms for Business Intelligence

Jérôme Darmont (Université Lumière Lyon 2, France) and Sabine Loudcher (Université Lumière Lyon 2, France)
Release Date: August, 2018|Copyright: © 2019 |Pages: 313
ISBN13: 9781522549635|ISBN10: 1522549633|EISBN13: 9781522549642|DOI: 10.4018/978-1-5225-4963-5


Because efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations, data analysis is an important part of modern business administration. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses.

Utilizing Big Data Paradigms for Business Intelligence is a pivotal reference source that provides vital research on how to address the challenges of data extraction in business intelligence using the five “Vs” of big data: velocity, volume, value, variety, and veracity. This book is ideally designed for business analysts, investors, corporate managers, entrepreneurs, and researchers in the fields of computer science, data science, and business intelligence.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Data Mining
  • Data Privacy
  • Data Warehouses
  • Heterogeneous Computing
  • Machine Learning
  • Mobility
  • Multi-User Communication
  • Trajectory Clustering
  • Trend Analysis

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Jérôme Darmont received his Ph.D. in computer science from the University of Clermont-Ferrand II, France in 1999. He joined the University of Lyon 2, France in 1999 as an associate professor, and became full professor in 2008. He was head of the Decision Support Databases research group within the ERIC laboratory from 2000 to 2008, and has been director of the Computer Science and Statistics Department of the School of Economics and Management since 2003. His current research interests mainly relate to performance in database management systems and data warehouses (performance optimization, auto-administration, benchmarking...), but also include XML and complex data warehousing and mining, and medical or health-related applications.
Sabine Loudcher is a full professor in Computer Science in a research lab of data science and business intelligence of the University of Lyon (France). She received her PhD degree in Computer Science from the University of Lyon in 1996 and since 2015, she is a full professor. From 2003 to 2012, she was the Assistant Director of the ERIC laboratory. Now, she leads a scientific axis of the Institute of Human Sciences of Lyon (MSH LSE) and she manages the Master of Digital Humanities of the University of Lyon (France). She carries out research on OLAP and Data Mining. She is more interested about data coming from documents or social networks. Her current work focuses on Graph OLAP, Text OLAP and Text Mining. She is involved in several projects especially in Digital Humanities with Social Sciences researchers.