Utilizing Big Data Paradigms for Business Intelligence

Introduction

Business intelligence (BI) aims to support decisions, not only in the business area stricto sensu, but also in the domains of health, environment, energy, transportation, science, etc. It provides a transverse vision of an organization's data and allows accessing quickly and simply to strategic information. For this sake, data must be extracted, grouped, organized, aggregated and correlated with methods and techniques such as data integration (ETL), data warehousing, online analytical processing (OLAP), reporting, data mining and machine learning. BI is nowadays casually used both in large companies and organizations, and small and middle-sized entreprises, thanks to the advent of cloud computing and cheap BI-as-a-service. The development of BI in the 1990's has also sparkled vivid research that currently addresses new challenges in big data.

Objective of the Book

Mashing up internal and external data is acknowledged as the best way to provide the most complete view for decision making. Yet, tackling data heterogeneity has always been an issue. With big data coming into play, benefits from processing external data look even better, but issues are also more complex. Data volume challenges even warehouses that were tailored for large amounts of data. Velocity challenges the very idea of materializing historicized data. Variety and veracity issues remain, but at a much greater extent. Finally, actually extracting intelligible information from big data (data value) requires novel methods. Finally, new technologies such as cloud computing, Hadoop/Spark and NoSQL databases also question classical BI.

This book plans to gather top-level research contributions addressing problems related to the five "Vs" of big data, technological issues, as well as big data analytics applications. Contributions will be reviewed by an international scientific committee.

Target Audience

The target audience of this book will be composed of

researchers
practitioners from the industry
graduate students in the fields of computer science, data science and business intelligence.

Submission Procedure

Researchers and practitioners are invited to submit on or before April 30, 2017 a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by May 1st, 2017 about the status of their proposals and sent chapter guidelines. Full chapters of about 10,000 words are expected to be submitted by June 30, 2017, and all interested authors must consult the guidelines for manuscript submissions prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Utilizing Big Data Paradigms for Business Intelligence. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the E-Editorial Discovery™ online submission manager.

Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. For additional information regarding the publisher, please visit www.igi-global.com. This publication is anticipated to be released in 2018.

Important Dates

April 30, 2017: Proposal Submission 3rd Deadline
May 1, 2017: Notification of Proposal Acceptance
June 30, 2017: Full Chapter Submission
October 30, 2017 November 15, 2017: Review Results Returned
November 30, 2017 December 15, 2017: Revised Chapter Submission
December 15, 2017 January 8, 2018: Final Acceptance Notification
December 30, 2017 January 15, 2018: Final Chapter Submission
February 6, 2018: Second Call for Chapters
March 9, 2018: 2nd call Full Chapter Submission
March 15, 2018: Book in queue to be copy edited
April 19, 2018: Proofreading complete
July 31, 2018: Book published!

Scientific committee

Alberto Abello, Universitat Politècnica de Catalunya, Barcelona, Spain
Antonio Badia, University of Louisville, USA
Fatma Boualli, Université Lille 2, France
Stéphane Bressan, National University of Singapore, Singapore
Arnaud Castelltort, Université de Montpellier, France
Karen Davis, Miami University, Oxford, USA
John W. Emerson, Yale University, USA
Pedro Furtado, Universidade de Coimbra, Portugal
Amine Ghrab, Université Libre de Bruxelles, Belgium
Anastasios Gounaris, Aristotle University of Thessaloniki, Greece
Le Gruenwald, University of Oklahoma, USA
Zhen He, La Trobe University, Australia
Chang-Shing Lee, National University of Tainan, Taiwan
Daniel Lemire, Université du Québec, Montréal, Canada
Patrick Marcel, Université de Tours, France
Morten Middelfart, Lumina Analytics, USA
Richi Nayak, Queensland University of Technology, Australia
Franck Ravat, Université de Toulouse, France
Alkis Simitsis, HP Labs, USA
Won-Kyung Sung, KISTI, South Korea
Thomas Tamisier, Luxembourg Institute of Science and Technology, Luxembourg
Christian Thomsen, Aalborg University, Denmark
Panos Vassiliadis, University of Ioannina, Greece
Robert Wrembel, Poznan University of Technology, Poland
Roberto Zicari, Frankfurt University, Germany
Iryna Zolotaryova, Kharkiv National University of Economics, Ukraine

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