Call for Chapters: Utilizing Big Data Paradigms for Business Intelligence

Editors

Jérôme Darmont & Sabine Loudcher
Université de Lyon, Lyon 2, ERIC EA3083
France

Call for Chapters

Proposals Submission Deadline: February 9, 2018
Full Chapters Due: March 9, 2018
Submission Date: March 9, 2018

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

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

This book mainly targets:
- researchers
- practitioners from the industry
- graduate students in the fields of computer science, data science and business intelligence.


Recommended Topics

- 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 March 9, 2018 a full chapter proposal of about 10,000 words. All interested authors must consult the guidelines for manuscript submissions at http://www.igi-global.com/publish/contributor-resources/before-you-write// 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.

There are no submission or acceptance fees for manuscripts submitted to this book publication, Trust in Knowledge Management and Systems in Organizations. All manuscripts are accepted based on a double-blind peer review editorial process.

All chapters must be submitted through the E-Editorial DiscoveryTM online submission manager. Please use the following link to submit your full chapter to this publication: https://www.igi-global.com/submission/submit-chapter/?projectid=819db81b-c3d2-4c59-9bc8-42b5135d0adb.


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

March 9, 2018: Full Chapter Submission
March 9, 2018 - March 25, 2018: Review Process
March 30, 2018: Review Results Returned to Authors
April 9, 2018: Revised Chapter Submission
April 13, 2018: Final Notification of Acceptance


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