The contents of the latest issue of: International Journal of Data Warehousing and Mining (IJDWM) Official Publication of the Information Resources Management Association Volume 5, Issue 4, October-December 2009 Published: Quarterly in Print and Electronically ISSN: 1548-3924; EISSN: 1548-3932 Published by IGI Publishing, Hershey-New York, USA www.igi-global.com/ijdwm Editor-in-Chief: David Taniar, Monash University, Australia Special Issue: Data Warehouse and Knowledge Discovery (DAWAK'08) GUEST EDITORIAL PREFACE Data Warehouse and Knowledge Discovery (DAWAK'08) Tho Manh Nguyen, Vienna University of Technology, Austria Johann Eder, University of Klagenfurt, Austria Il-Yeol Song, Drexel University, USA Data warehousing and knowledge discovery has been widely accepted as key technologies for enterprises and organizations to improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision making process, the data to be processed becomes more and more complex in both structure and semantics. During the past years, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has become one of the most important international scientific events to bring together researchers, developers, and practitioners. This year's conference built on the tradition of facilitating the cross-disciplinary exchange of ideas, experience, and potential research directions. The articles in this special issue of IJDWM address problems of referential horizontal partitioning in relational data warehouses and propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. To read the guest editorial preface, please consult this issue of IJDWM in your library. PAPER ONE Referential Horizontal Partitioning Selection Problem in Data Warehouses: Hardness Study and Selection Algorithms Ladjel Bellatreche, University of Poitiers, France Kamel Boukhalfa, University of Poitiers, France Pascal Richard, University of Poitiers, France Komla Yamavo Woameno, University of Poitiers, France Horizontal partitioning has been largely adopted by the database community, where it took a significant part in the physical design process. Actually, it is supported by most commercial database systems (DBMS), where a native data definition language for decomposing tables/materialized views using various modes is proposed. In traditional databases, horizontal partitioning has been largely studied, where several fragmentation algorithms were proposed to partition tables in isolation. In this article, the authors focus on the evolution of horizontal partitioning in commercial DBMS motivated by decision support applications and formalize the referential fragmentation schema selection problem in the data warehouse. This article studies hardness to select an optimal solution and develops two algorithms due to high complexity. The authors also conduct extensive experimental studies using the data set of APB1 benchmark to compare the quality the proposed algorithms using a mathematical cost model. Based on these experiments, recommendations are given to advise database administrators for well using horizontal partitioning. To obtain a copy of the entire article, click on the link below. http://infosci-on-demand.com/content/details.asp?ID=34921 PAPER TWO What-if Simulation Modeling in Business Intelligence Matteo Golfarelli, DEIS - University of Bologna, Italy Stefano Rizzi, DEIS - University of Bologna, Italy Optimizing decisions has become a vital factor for companies. In order to be able to evaluate beforehand the impact of a decision, managers need reliable provisional systems. Though data warehouses enable analysis of past data, they are not capable of giving anticipations of future trends. What-if analysis fills this gap by enabling users to simulate and inspect the behavior of complex system under given hypotheses. A crucial issue in the design of what-if applications is to find an adequate formalism to conceptually express the underlying simulation model. In this article, the authors report on how, within the framework of a comprehensive design methodology, this can be accomplished by extending UML 2 with a set of stereotypes. Their proposal is centered on the use of activity diagrams enriched with object flows and aimed at expressing functional, dynamic, and static aspects in an integrated fashion. The authors provide examples taken from a real case study in the commercial area. To obtain a copy of the entire article, click on the link below. http://infosci-on-demand.com/content/details.asp?ID=34922 PAPER THREE A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature Min Song, New Jersey Institute of Technology, USA Xiaohua Hu, Drexel University, USA Illhoi Yoo, University of Missouri, USA Eric Koppel, New Jersey Institute of Technology, USA As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this article, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors develop and apply a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm. The authors evaluate the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. To obtain a copy of the entire article, click on the link below. http://infosci-on-demand.com/content/details.asp?ID=34923 PAPER FOUR Influence of Domain and Model Properties on the Reliability Estimates' Performance Zoran Bosni?, University of Ljubljana, Slovenia Igor Kononenko, University of Ljubljana, Slovenia In machine learning, the reliability estimates for individual predictions provide more information about individual prediction error than the average accuracy of predictive model (e.g. relative mean squared error). Such reliability estimates may represent decisive information in the risk-sensitive applications of machine learning (e.g. medicine, engineering, and business), where they enable the users to distinguish between more and less reliable predictions. In this paper, the authors empirically analyze the dependence of reliability estimates' performance on the data set and model properties. To obtain a copy of the entire article, click on the link below. http://infosci-on-demand.com/content/details.asp?ID=34924 ***************************************************** For full copies of the above articles, check for this issue of the International Journal of Data Warehousing and Mining (IJDWM) in your institution's library. This journal is also included in the IGI Global aggregated "InfoSci-Journals" database: www.infosci-journals.com. ***************************************************** CALL FOR PAPERS Mission of IJDWM: The International Journal of Data Warehousing and Mining (IJDWM) publishes and disseminates knowledge on an international basis in the areas of data warehousing and data mining. It is published multiple times a year, with the purpose of providing a forum for state-of-the-art developments and research, as well as current innovative activities in data warehousing and mining. In contrast to other journals, this journal focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real world problems. The journal is targeted at both academic researchers and practicing IT professionals. Coverage of IJDWM: Data mart and practical issues Data mining methods Data models Data structures Design data warehousing process Online analytical process Tools and languages The journal is devoted to the publication of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. Interested authors should consult the journal's manuscript submission guidelines at www.igi-global.com/ijdwm. All inquiries and submissions should be sent to: Editor-in-Chief: Dr. David Taniar at david.taniar@infotech.monash.edu.au