[APDIO] Fwd: Call for Proposal submission - Integration Challenges for Analytics, Business Intelligence, and Data Mining

[Isabel Cristina da Silva Lopes <cristinalopes@iscap.ipp.pt>]

 

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Call for Chapters: Integration Challenges for Analytics, Business Intelligence, and Data Mining

Editors

Ana Azevedo, CEOS.PP / ISP / P.Porto, Portugal
Manuel Filipe Santos, Algoritmi Research Center, Portugal

Call for Chapters

Proposals Submission Deadline: February 1, 2020
Full Chapters Due: March 14, 2020
Submission Date: April 25, 2020

Introduction

Business Intelligence (BI) is one area of the Decision Support Systems (DSS) discipline and can be defined as the process that transforms data into information and then into knowledge (Golfarelli, Rizzi & Cella, 2004). Being rooted in the DSS discipline, BI has suffered a considerable evolution over the last years and is, nowadays, an area of DSS that attracts a great deal of interest from both the industry and researchers (Arnott & Pervan, 2008; Clark, Jones & Armstrong, 2007; Davenport, 2010; Hannula & Pirttimäki, 2003; Hoffman, 2009; Negash, 2004; Richardson, Schlegel & Hostmann, 2009; Richardson, Schlegel, Hostmann & McMurchy, 2008; Sallam, Hostman, Richardson & Bitterer, 2010). A BI system is a particular type of system. One of the main aspects is that of user-friendly tools, that makes systems truly available to the final business user.
Analytics is a topic of growing interest in the research community. INFORMS defines analytics as the scientific process of transforming data into insights with the purpose of making better decisions. INFORMS also classifies analytics into three different types, namely, descriptive analytics, predictive analytics, and prescriptive analytics. These three levels of Analytics are not exclusive, overlapping each other many times. Sharda, Delen & Turban (2018), identify Business Intelligence with Descriptive Analytics, identifying the other two types of analytics as Advanced Analytics. Nevertheless, the editors of this book consider that important value is loss without the integration of Data (both structured and unstructured) Mining in Business Intelligence Systems.
DM integration with BI systems can be tackled from different perspectives. On the one hand, it can be considered that the effective integration of DM with BI systems must involve final business users’ access to DM models. This access is crucial in order to business users to develop an understanding of the models, to help them in decision making. Han and Kamber state that the integration (coupling) of DM with database systems and/or data warehouses is crucial in the design of DM systems (Han & Kamber, 2006). They consider four possible integration schemes, which are, in increasing order of integration: no coupling, louse coupling, semi-tight coupling, and tight coupling. They present the concept of On-Line Analytical Mining (OLAM), which incorporates OLAP with DM, as a way to achieve tight coupling. On the other hand, a different approach can be considered, through the outgrowth of new strategies that allow business users and DM specialists developing new communication strategies. Wang and Wang introduce a model that allows knowledge sharing among business insiders and DM specialists (Wang & Wang, 2008). It is argued that this model can make DM more relevant to BI.

References
Arnott, D. & Pervan, G. (2008). Eight Key Issues for the Decision Support Systems Discipline. Decision Support Systems, 44(3), 657-672.
Clark, T. D., Jones, M. C. & Armstrong, C.P. (2007). The Dynamic Structure of Management Support Systems: Theory Development, Research, Focus, and Direction. MIS Quarterly, 31(3), 579-615.
Davenport, T. H. (2010). Business Intelligence and Organizational Decisions. International Journal of Business Intelligence Research, 1(1), 1-12.
Han, J. & Kamber, M. (2006). Data Mining: concepts and Techniques. San Francisco, CA: Morgan Kaufman Publishers.
Hannula, M. & Pirttimäki, V. (2003). Business Intelligence Empirical Study on the Top 50 Finnish Companies. Journal of American Academy of Business, 2(2), 593-599.
Hoffman, T. (2009). 9 Hottest Skills for '09. Computer World, January 1(1), 26-27.
Negash, S. (2004). Business Intelligence. Communications of the Association for Information Systems, 13(1), 177-195.
Richardson, J., Schlegel, K. & Hostmann, B. (2009). Magic Quadrant for Business Intelligence Platforms - 2009. Core Research Note: G00163529, Gartner.
Richardson, J., Schlegel, K., Hostmann, B. & McMurchy, N. (2008). Magic Quadrant for Business Intelligence Platforms - 2008. Core Research Note: G00154227, Gartner.
Sallam, R., Hostman, B., Richardson, J. & Bitterer, A. (2010). Magic Quadrant for Business Intelligence Platforms 2010. Core Research Note: G00173700, Gartner.
Sharda, R., Delen, D. & Turban, E. (2018). Business Intelligence: A Managerial Approach, fourth edition. Upper Sadle River, NJ: Pearson Prentice Hall.
Wang, H. & Wang, S. (2008). A Knowledge Management Approach to Data Mining Process for Business Intelligence. Industrial Management & Data Systems, 108(5), 622-634.

Objective

The primary objective of this book is to provide insights concerning the integration of data mining in business intelligence and analytics systems. This is a cutting-edge and important topic that deserves a reflexion, and this book is an excellent opportunity to do it. The book also aims to provide the opportunity for a reflexion on this important issue, increasing the understanding of using data mining in the context of business intelligence and analytics, providing relevant academic work, empirical research findings, and an overview of this relevant field of study.

Target Audience

The target audience of this book will be composed of professionals in the area of data mining, business intelligence, and analytics, managers, researchers, academicians, practitioners, and graduate students.

Recommended Topics

Recommended topics include, but are not limited to, the following:
- Trends in using Data Mining, Business Intelligence and Analytics;
- Models for Data Mining integration with Business Intelligence and Analytics;
- Methodologies for Data Mining integration with Business Intelligence and Analytics;
- Analysis of applications of Data Mining in the context of Business Intelligence;
- Data Mining standards and Languages for Business Intelligence;
- Adaptive business intelligence (with optimization);
- Data intelligence
- Data science;
- Business Analytics;
- Descriptive, predictive, and prescriptive machine learning:
- Artificial Intelligence.

Submission Procedure

Researchers and practitioners are invited to submit on or before February 1, 2020, 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 February 15, 2020 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by March 14, 2020, and 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.
Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Integration Challenges for Analytics, Business Intelligence, and Data Mining. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery®TM 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 2021.

Important Dates

February 1, 2020: Proposal Submission Deadline
February 15, 2020: Notification of Acceptance
March 14, 2020: Full Chapter Submission
April 11, 2020: Review Results Returned
April 25, 2020: Final Acceptance Notification
May 1, 2020: Final Chapter Submission

Inquiries

Ana Azevedo, CEOS.PP / ISCAP / P.Porto, Portugal
Manuel Filipe Santos, Algoritmi Research Center, Portugal

aazevedo@iscap.ipp.pt

Classifications

Business and Management; Computer Science and Information Technology

Propose a Chapter : https://www.igi-global.com/publish/call-for-papers/submit/4595