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.
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.
The target audience of this book will be composed of
Recommended topics include, but are not limited to, the following:
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.
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.
For any Inquiry, please contact us.
|Chapter Proposals||Accepted Proposals||Chapter Submissions||Average Number of Reviews per Chapter||Accepted Chapters (after revision)|
Torben Bach Pedersen, Aalborg University, Denmark
David Taniar, Monash University, Australia
Jérôme Darmont, Université de Lyon, France
Sabine Loudcher, Université de Lyon, France
Prakhar Mehrotra, Uber Technologies, USA
Leila Abidi, Université Sorbonne Paris Cité, France
Hanene Azzag, Université Sorbonne Paris Cité, France
Salima Benbernou, Université Sorbonne Paris Cité, France
Mehdi Bentounsi, Université Sorbonne Paris Cité, France
Christophe Cérin, Université Sorbonne Paris Cité, France
Tarn Duong, Université Sorbonne Paris Cité, France
Philippe Garteiser, Inserm U1149 and Université Sorbonne Paris Cité, France
Mustapha Lebbah, Université Paris 13, France
Mourad Ouziri, Université Sorbonne Paris Cité, France
Soror Sahri, Université Sorbonne Paris Cité, France
Michel Smadja, SISNCOM, France
Eleazar Leal, University of Minnesota Duluth, USA
Le Gruenwald, University of Oklahoma, USA
Jianting Zhang, City College of New York, USA
Rajendra Akerkar, Western Norway Research Institute, Norway
Mickaël Martin-Nevot, Aix-Marseille Université, France
Sébastien Nedjar, LIF, Aix-Marseille Université, France
Lotfi Lakhal, LIF, Aix-Marseille Université, France
Rosine Cicchetti, LIF, Aix-Marseille Université, France
Youssef Hmamouche, LIS, Aix-Marseille Université, France
Piotr Marian Przymus, LIF, Aix-Marseille Université, France
Hana Alouaoui, LIF, Aix-Marseille Université, France
Alain Casali, LIF, Aix-Marseille Université, France
Lotfi Lakhal, LIF, Aix-Marseille Université, France
Isaac Osesina, Aware Inc., USA
Eduard Tudoreanu, University of Arkansas at Little Rock, USA
John P. McIntire, USAF AFRL, USA
Paul R. Havig, USAF AFRL, USA
Eric E. Geiselman, AFRL 711HPW/RHCV, USA
Kuo-Wei Hsu, National Chengchi University, Taiwan
Yung-Chang Ko, China Steel Corporation, Taiwan