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BDBI 2017 : 3rd CFC: Utilizing Big Data Paradigms for Business Intelligence


When Apr 30, 2017 - Mar 30, 2018
Where N/A
Abstract Registration Due Apr 30, 2017
Submission Deadline Jun 30, 2017
Notification Due Oct 30, 2017
Final Version Due Dec 30, 2017
Categories    big data   data mining   data warehousing   dataviz

Call For Papers

Proposal Submission Deadline: April 30, 2017

Utilizing Big Data Paradigms for Business Intelligence

A book edited by Jerome Darmont and Sabine Loudcher (Université de Lyon, Lyon 2, ERIC EA3083, France)

* 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.

* Recommended topics include, but are not limited to, the following:

- Data volume issues: physical data management, scalability issues, performance optimization, NoSQL storage
- Data variety issues: information retrieval, complex data preparation/ETL, data lakes, metadata extraction and management, semantics, linked data...
- Data velocity issues: cloud/parallel processing for analytics...
- Data veracity issues: data quality, data security (privacy, integrity...),
- Data value issues: data visualization, data storytelling, personalization, recommendation, collaborative analyses...
- Applications: Internet of Things & BI, Textual Documents Analytics, Social Media Analytics, Real-time analytics, Self-service BI, Smart cities & BI, Big Data Analytics in Healthcare, Social BI, Open Data, Digital Humanities...

* Submission Procedure

Researchers and practitioners are invited to submit on or before February 28, 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 March 17, 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 at 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 DiscoveryTM online submission manager (cf. ).

* 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 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: Review Results Returned
November 30, 2017: Revised Chapter Submission
December 15, 2017: Final Acceptance Notification
December 30, 2017: Final Chapter Submission

* Scientic Committee

Alberto Abello,Universitat Politenica de Catalunya, Barcelona, Spain
Antonio Badia, University of Louisville, USA
Fatma Boualli, Universite Lille 2, France
Stephane Bressan, National University of Singapore, Singapore
Arnaud Castelltort, Universite de Montpellier, France
Karen Davis, University of Cincinnati, USA
John W. Emerson, Yale University, USA
Pedro Furtado, Universidade de Coimbra, Portugal
Amine Ghrab, Universite 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, Universite du Quebec, Montreal, Canada
Patrick Marcel, Universite François Rabelais de Tours, France
Morten Middelfart, TARGIT, USA
Richi Nayak, Queensland University of Technology, Australia
Franck Ravat, Universite 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 Technical University, Poland
Roberto Zicari, Frankfurt University, Germany
Iryna Zolotaryova, Kharkiv National University of Economics, Ukraine

* Inquiries can be forwarded to

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