*CALL FOR CHAPTER PROPOSALS * *Proposal Submission Deadline: July 30, 2013*
* * *Integration of Data Mining in Business Intelligence Systems* * * *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 *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. * * *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. * * *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. * * *Submission Procedure * Researchers and practitioners are invited to submit *on or before July 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 *August 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. * * *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. * * *Important Dates* *July 30, 2013: *Proposal Submission Deadline** *August 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 ** * * *Editorial Advisory Board* 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 * * * * *Inquiries and submissions can be forwarded electronically (Word document) to:* Dr. Ana Azevedo E-mail: [email protected]
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