International Journal of Business Intelligence and Data Mining

Special Issue on

OLAP Intelligence: Meaningfully Coupling OLAP and
Data Mining Tools and Algorithms

Editor

Alfredo Cuzzocrea
 

Publisher

[Aim and Scope | Submission Guidelines and Instructions | Important Dates | Program Committee]

ACCEPTED PAPERS

• Ronnie Alves, Joel Riberio, Orlando Belo, Mining Significant Change Patterns in Multidimensional Spaces
   

• Marc Plantevit, Anne Laurent, Maguelonne Teisseire, Mining Convergent and Divergent Sequences in Multidimensional Data
   

• Sebastien Nedjar, Alain Casali, Rosine Cicchetti, Lotfi Lakhal, Reduced Representations of Emerging Cubes for OLAP Database Mining
   

• Alfredo Cuzzocrea, Jerome Darmont, Hadj Mahboubi, Fragmenting Very Large XML Data Warehouses via K-Means Clustering Algorithm
   

• Veronique Cariou, Jerome Cubille, Christian Derquenne, Sabine Goutier, Francoise Guisnel, Henri Klajnmic, Embedded Indicators to Facilitate the Exploration of a Data Cube
   

• Maurizio Pighin, Lucio Ieronutti, A Novel Visualization Technique for Exploring Multidimensional Data

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Aim and Scope

Nowadays, it is widely recognized that OLAP technology provides powerful analysis tools for extracting useful knowledge from large amounts of data stored in different and highly-heterogeneous formats, and very often distributed across networked settings ranging from conventional wired environments to innovative wireless and P2P networks. Several advantages confirm the benefits coming from such an analysis model: (i) the amenity of “naturally” representing real-life data sets that are multi-level, multidimensional, and highly-correlated in nature; (ii) the amenity of analyzing multidimensional data according to a multi-resolution vision; (iii) the rich availability of a wide class of powerful OLAP operators (such as roll-up, drill-down, slice-&-dice etc) and queries (e.g., range-, top-k, iceberg and gradient queries); (iv) the integration of OLAP with more complex analysis tools coming from statistics, time series analysis, and Data Mining.

An elegant and successful solution in this line of research consists in coupling OLAP and Data Mining tools and algorithms, which is the basis of the so-called OLAM – OnLine Analytical Mining model, proposed by Jiawei Han in his seminal paper in 1997. Basically, this proposal consists in meaningfully combining the powerful of OLAP with the effectiveness of Data Mining tools and algorithms capable of discovering interesting knowledge from large amounts of data (e.g., the data cell set of a given OLAP data cube) by means of clustering, classification, association rule discovery, frequent item set mining, and so forth.

During the last decade, researchers have devoted their attention on the issue of meaningfully coupling OLAP and Data Mining tools and algorithms, leading to the term “OLAP Intelligence”, which can be reasonable considered as one of the emerging research topics of next years in the context of knowledge discovery methodologies. This great interest is essentially due to both exciting theoretical perspectives, such as complexity issues of executing time-consuming Data Mining routines over very large OLAP data cubes, and relevant application issues, which have a great impact in a plethora of real-life scenarios ranging from conventional distributed database management systems and cooperative information systems to innovative data stream management systems and sensor network data analysis tools.

Despite these efforts, many aspects need to be further investigated in order to achieve a reliable convergence between OLAP and Data Mining, thus making this technology a reference for next-generation data-intensive analysis tools. Among those, we recall:

• Data Warehouse Support for OLAM Architectures

• Database Support for OLAM Architectures

• Complex Knowledge Representation Models for Data Cubes in OLAM

• Complex Knowledge Reasoning Models for Data Cubes in OLAM

• OLAP Data Cube Integration

• Advanced Clustering Algorithms for Very Large OLAP Data Cubes

• Advanced Classification Algorithms for Very Large OLAP Data Cubes

• Advanced Association Rule Discovery Algorithms for Very Large OLAP Data Cubes

• Advanced Frequent Item Set Mining Algorithms for Very Large OLAP Data Cubes

• OLAM over Multiple Data Sources

• OLAM over Highly-Heterogeneous Data Sources

• OLAM over High-Dimensional Datasets

• Multi-Cube Mining Algorithms

• Multi-Layer Mining Algorithms for OLAP Data Cubes

• Mixture Models in OLAM

• OLAM over Imprecise/Incomplete Data Sources

• Statistical Tools for Very Large OLAP Data Cubes

• Probabilistic Tools for Very Large OLAP Data Cubes

• Privacy Preserving OLAP

• OLAP Visualization

• Intelligent Clustering Methodologies for Large Sets of OLAP Data Cells

• Feature Selection for Data Mining Algorithms on OLAP Data Cubes

• Data-Mining-Aided OLAP Browsing

• Data-Mining-Aided OLAP Exploration

• Data-Mining-Aided Interactive Analysis of Very Large OLAP Data Cubes

• Machine Learning for OLAP

• Ensemble Analysis of Mining Results Extracted From Very Large OLAP Data Cubes

• Intelligent Interpretation of OLAM Results

• Constraint-based OLAM

• Performance Issues for OLAP (e.g., Data Cube Compression Algorithms)

• Query Languages for OLAM

• Query Evaluation Plans for Complex OLAM Procedures

• Integration of SQL with OLAM Procedures

• Novel OLAM Paradigms

• OLAM in Specialized Context: Web, XML, RDF, Ontology Bases, Data Stream, Sensor Network Data, RFID, Peer-To-Peer, Process-Log Repositories, Workflow Management Systems, E-Commerce, B2B, B2C etc

The Special Issue “OLAP Intelligence: Meaningfully Coupling OLAP and Data Mining Tools and Algorithms” of the International Journal of Business Intelligence and Data Mining, InderScience Publishers, will explore these research themes and will be focused on theoretical foundations as well as innovative models, techniques, algorithms and applications of OLAP Intelligence.

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Submission Guidelines and Instructions

Submitted papers should not be currently under consideration for publication elsewhere. Submission process includes abstract and paper submission.

Abstracts (deadline June 20, 2008) should be sent by e-mail (preferably in an enclosed MS Word file) to the Special Issue Editor Alfredo Cuzzocrea at cuzzocrea@si.deis.unical.it. Abstracts must include paper title, abstract, list of keywords, and list of authors with full names and affiliations. One of the authors must be designated as the primary contact point to receive notification and reviews.

Papers (deadline June 30, 2008) should be submitted in PDF or Postscript format using the Online Submissions of Papers. If you experience any problems submitting your paper online, please contact submissions@inderscience.com, describing the exact problem you experience. Please include in your email the title “IJBIDM - Special Issue on OLAP Intelligence”. A guide for authors, sample copies and other relevant information for submitting papers are available in the Full Submission Guidelines Web page.

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Important Dates

Abstract Submission: June 20, 2008
Paper Submission: June 30, 2008
Paper Acceptance/Rejection Notification - First Round: August 31, 2008
Revised Paper Submission: November 15, 2008
Final Paper Acceptance/Rejection Notification: December 31, 2008
Camera-Ready Versions of Accepted Papers Submission: January 31, 2008
IJBIDM Special Issue Publication: June 2009

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Program Committee Chair

Alfredo Cuzzocrea, ICAR Institute & University of Calabria, Italy

Program Committee

Alberto Abello, Polytechnical University of Catalunya, Spain
Yuan An, Drexel University, PA, USA
Antonio Badia, University of Louisville, KY, USA
Ladjel Bellatreche, LISI Laboratory, ENSMA, France
Jerome Darmont, ERIC Laboratory, University Lyon 2, France
Karen C. Davis, University of Cincinnati, OH, USA
Todd Eavis, Concordia University, Canada
Joseph Fong, City University of Hong Kong, China
Pedro Furtado, University of Coimbra, Portugal
Matteo Golfarelli, University of Bologna, Italy
Carlos Hurtado, University of Chile, Chile
Jens Lechtenborger, University of Munster, Germany
Jason Li, Drexel University, PA, USA
Pat Martin, Queen's University, Ontario, Canada
Rokia Missaoui, University of Quebec, Quebec, Canada
Muhesh Mohania, IBM India Research Lab, India
Mirek Riedewald, Cornell University, NY, USA
Timos Sellis, National Technical University of Athens, Greece
Alkis Simitsis, Stanford University, CA, USA
Manolis Terrovitis, The University of Hong Kong, China
Igor Timko, Free University of Bozen-Bolzano, Italy
Juan Trujillo, University of Alicante, Spain
Wei Wang, University of New South Wales, Australia
Robert Wrembel, Poznan University of Technology, Poland

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