Utilizing Big Data Paradigms for Business Intelligence, 1st Edition

  • Jérôme Darmont
  • Sabine Loudcher
  • Published By: Business Science Reference
  • ISBN-10: 1522549641
  • ISBN-13: 9781522549642
  • DDC: 658.4
  • Grade Level Range: College Freshman - College Senior
  • 313 Pages | eBook
  • Original Copyright 2019 | Published/Released January 2019
  • This publication's content originally published in print form: 2019
  • Price:  Sign in for price



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. This title 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.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Advances in Business Information Systems and Analytics (ABISA) Book Series.
Titles in This Series.
Editorial Advisory Board.
Table of Contents.
Detailed Table of Contents.
1: Applications of Artificial Intelligence in the Realm of Business Intelligence.
2: A Big Data Platform for Enhancing Life Imaging Activities.
3: A Survey of Parallel Indexing Techniques for Large-Scale Moving Object Databases.
4: Privacy and Security in Data-Driven Urban Mobility.
5: C-Idea: A Fast Algorithm for Computing Emerging Closed Datacubes.
6: Large Multivariate Time Series Forecasting: Survey on Methods and Scalability.
7: Exploring Multiple Dynamic Social Networks in Computer-Mediated Communications: An Experimentally Validated Ecosystem.
8: Analysis of Operation Performance of Blast Furnace With Machine Learning Methods.
Compilation of References.
About the Contributors.