[AISWorld] LAST CALL FOR CHAPTER PROPOSALS - Integration of Data Mining in Business Intelligence Systems

[Ana Azevedo]

*LAST CALL FOR CHAPTER PROPOSALS *

*Proposal Submission Deadline: September 30, 2013*

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*Integration of Data Mining in Business Intelligence Systems*

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*A book edited by:*

Ana Azevedo, *Algoritmi R&D Center/University of Minho and Polytechnic
Institute of Porto/ISCAP, Portugal*
Manuel Filipe Santos, *Algoritmi R&D Center/University of Minho, Portugal*





To be published by IGI Global:

http://www.igi-global.com/publish/call-for-papers/call-details/1032



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*Introduction*

Business Intelligence (BI) is one area of the Decision Support Systems
(DSS) discipline and refers to information systems aimed at integrating
structured and unstructured data in order to convert it into useful
information and knowledge, upon which business managers can make more
informed and consequently better decisions. 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. 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.

The term Knowledge Discovery in Databases (KDD) was coined in 1989 to refer
to the broad process of finding knowledge in data, and to emphasize the
"high-level" application of particular data mining (DM) methods (Fayyad,
Piatetski-Shapiro & Smyth, 1996). The DM phase concerns, mainly, to the
means by which patterns are extracted and enumerated from data.

DM is being applied with success in BI and several examples of applications
can be found. Despite that, DM has not yet reached to non-specialized users
and thus it is not yet completely integrated with BI. Powerful analytical
tools, such as DM, remain too complex and sophisticated for the average
consumer of BI systems. McKnight supports that bringing DM to the front
line business personnel will increase their potential to attaining BI's
high potential business value (McKnight, 2002). Another fundamental issue
that is pointed out by McKnight is the capability of DM tools to be
interactive, visual, and understandable, to work directly on the data, and
to be used by front line workers for intermediate and lasting business
benefits. Currently, DM systems are functioning as separate isles, and
hereby it is considered that only the full integration of the KDD process
on BI can conduct to an effective usage of DM in BI (Azevedo & Santos,
2011). Three main reasons can be pointed out for DM to be not completely
integrated with BI, each one leading to a specific problem that constraints
DM usage in BI. Firstly, the models/patterns obtained from DM are complex
and there is the need of an analysis from a DM specialist. This fact can
lead to a non-effective adoption of DM in BI, being that DM is not really
integrated on most of the implemented BI systems, nowadays. Secondly, the
problem with DM is that there is not a user-friendly tool that can be used
by decision makers to analyze DM models. Usually, BI systems have
user-friendly analytical tools that help decision makers in order to obtain
insights on the available data and allow them to take better decisions.
Examples of such tools are On-Line Analytical Processing (OLAP) tools,
which are widely used. There are not equivalent tools for DM that allow
business users to obtain insights in DM models. Finally, but extremely
important, it has not been given sufficient emphasis to the development of
solutions that allow the specification of DM problems through business
oriented languages, and that are also oriented for BI activities. With the
expansion that has occurred in the application of DM solutions in BI, this
is, currently, of increasing importance.

BI systems are, usually, built on top of relational databases and diverse
types of languages are involved. As a consequence, DM integration with
relational databases is an important issue to consider when studying DM
integration with BI. Codd´s relational model for database systems (Codd,
1970; Codd, 1982) has been adopted long ago in organizations. One of the
reasons for the great success of relational databases is related with the
existence of a standard language - Structured Query Language (SQL). SQL
allows business users to obtain quick answers to ad-hoc business questions,
through queries on the data stored in databases. SQL is nowadays included
in all the Relational Database Management Systems (RDBMS). SQL serves as
the core above which are constructed the various Graphical User Interfaces
(GUI) and user friendly languages, such as Query-By-Example (QBE), included
in RDBMS. It is also necessary to define a standard language, which can
operate likewise for data mining. Several approaches have been proposed for
the definition of data mining languages. In the literature there can be
found some language specifications, namely, DMQL (Han, Fu, Wang, Koperski &
Zaiane, 1996), MINE RULE (Meo, Psaila & Ceri, 1998), MSQL (Imielinski &
Virmani, 1999), SPQL (Bonchi, Giannotti, Lucchesse, Orlando, Perego &
Trasarti, 2007), KDDML (Romei,  Ruggieri &  Turini, 2006), XDM (Meo &
Psaila, 2006), RDM (De Raedt, 2002),  QMBE (Azevedo & Santos, 2012), among
others.

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 (Azevedo &
Santos, 2012; Azevedo & Santos 2011). 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:

Azevedo, A. & Santos, M.F. (2011). A Perspective on Data Mining Integration
with Business Intelligence. In Kumar, A.  (Ed.), Knowledge Discovery
Practices and Emerging Applications of Data Mining: Trends and New Domains
(pp.109-129). Hershey, NY: IGI Publishing.

Azevedo, A. & Santos, M.F. (2012). Closing the Gap between Data Mining and
Business Users of Business Intelligence Systems: A Design Science Approach.
International Journal of Business Intelligence Research, 3(4), 14-53.

Bonchi, F.; Giannotti, F.; Lucchesse, C.; Orlando, S.; Perego, R. &
Trasarti, R. (2007). On Interactive Pattern Mining from Relational
Databases. In Dzeroski, S. & Struyf, J.  (Eds.), Lecture Notes on Computer
Science: Vol. 4747. Knowledge Discovery in Inductive Databases - 5th
International Workshop, KDID 2006 (pp. 42-62). Berlin, Heidelberg:
Springer-Verlag.

Codd, E. F. (1970). A Relational Model of Data for Large Shared Data Banks.
Communications of the ACM, 13(6), 377-387.

Codd, E. F. (1982). Relational Database: a Practical Foundation for
Productivity. Communications of the ACM, 25(2), 109-117.

De Raedt, L. (2002). Data Mining as Constraint Logic Programming. In Kakas,
A. C. & Sadri, F.  (Eds.), Lecture Notes on Artificial Intelligence: Vol.
2408. Computational Logic: Logic Programming and Beyond -  Essays in Honour
of Robert A. kowalski - Part II (pp. 526-547). Berlin, Heidelberg:
Springer-Verlag.

Fayyad, U. M., Piatetski-Shapiro, G. & Smyth, P. (1996). From data mining
to knowledge discovery: an overview. In Fayyad, U. M. , Piatetski-Shapiro,
G. , Smyth, P. & Uthurusamy, R.  (Eds.), Advances in knowledge discovery
and data mining (pp.1-34). Menlo Park, California: AAAI Press/The MIT Press.

Han, J., Fu, Y., Wang, W., Koperski, K. & Zaiane, O. (1996).  DMQL: A Data
Mining Query Language for Relational Databases. Proceedings of the
SIGMOD'96 Workshop on Research Issues on Data Minining and Knowledge
Discovery (DMKD'96), 27-34.

Imielinski, T. & Virmani, A. (1999). MSQL: A Query Language for Database
Mining. Data Mining and Knowledge Discovery, 3(4), 373-408.

McKnight, W. (2002). Bringing Data Mining to the Front Line, Part 1.
Information Management magazine, November(2002), Retrieved on July, 16th
2009 at http://www.information-management.com/issues/20021101/5980-1.html.

Meo, R. & Psaila, G. (2006). An XML-Based Database for Knowledge Discovery.
In Grust, T.; Höpfner, H. ; Illarramendi, A. ; Jablonski, S. ; Mesiti, M. ;
Müller, S. ; Patranjan, P. ; Sattler; Kai-Uwe; Spiliopoulou, M. & Wijsen, J.
(Eds.), Lecture Notes in Computer Science: Vol. 4254. Current Trends in
Database Technology - EDTB 2006 Workshops (pp. 814-828). Berlin,
Heidelberg: Springer-Verlag.

Meo, R., Psaila, G. & Ceri, S. (1998). An Extension to SQL for Mining
Association Rules. Data Mining and Knowledge Discovery, 2(2), 195-224.

Romei, A., Ruggieri, S. & Turini, F. (2006). KDDML: A Middleware Language
and System for Knowledge Discovery in Databases. Data & Knowledge
Engineering, 57(2), 179-220.

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 of the Book*

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

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*Target Audience *

Policy makers, academicians, researchers, advanced-level students,
technology developers, professionals in the area of data mining and
business intelligence, managers, and graduate students, are the target of
this book.

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

Contributors are welcome to submit chapters on the following topics
relating to data mining integration with business intelligence systems.
Recommended topics include, but are not limited to, the following:

-> Trends in using data mining and business intelligence systems;

-> Models for data mining integration with business intelligence;

-> Methodologies for data mining integration with business intelligence;

-> Analysis of applications of data mining in the context of business
intelligence;

-> Data mining standards and languages for business intelligence;

-> Data mining and business intelligence systems.

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*Submission Procedure *

Researchers and practitioners are invited to submit *on or before September
30, 2013*, a 2-3 page chapter proposal clearly explaining the mission and
concerns of his or her proposed chapter. Authors of accepted proposals will
be notified by *October 15, 2013* about the status of their proposals and
sent chapter guidelines. Full chapters are expected to be submitted by
*December
30, 2013*. All submitted chapters will be reviewed on a double-blind review
basis. Contributors may also be requested to serve as reviewers for this
project.

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*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
http://www.igi-global.com/publish/call-for-papers/call-details/1032)<http://www.igi-global.com/>.
This book is anticipated to be released in 2014.

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*Important Dates*

*September 30, 2013:    *Proposal Submission Deadline**

*October 15, 2013:            *Notification of Acceptance**

*December 30, 2013:      *Full Chapter Submission**

*March 15, 2014:                               *Review Results Returned**

*April 15, 2014:                   *Revised Chapter Submission**

*April 30, 2014:                  *Final Acceptance Notifications**

*May 15, 2014:                    *Submission of Final Chapter **

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*Editorial Advisory Board*



A.V.Senthil Kumar, Hindusthan College of Arts and Science, India

Alex S. Karakos, Democritus University of Thrace , Greece

Anthony Scime, The College at Brockport, State University of New York

Dumitru Dan BURDESCU, University of Craiova, Romania

Stavros Valsamidis, Kavala's Institute of Technology, Greece

Wiesław Pietruszkiewicz, West Pomeranian University of Technology in
Szczecin, Poland

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*Inquiries and submissions can be forwarded electronically (Word document)
to:*

Dr. Ana Azevedo

E-mail: ana.azevedo at gmail.com
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