-------- Forwarded Message --------
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
<https://www.igi-global.com/publish/call-for-papers/call-details/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>:
https://www.igi-global.com/publish/call-for-papers/submit/4595
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