-------- Original-Nachricht -------- Betreff: [isworld] CFP: IJBIDM Special Issue on "OLAP Intelligence" Datum: Sat, 19 Apr 2008 04:24:35 -0400 Von: Alfredo Cuzzocrea cuzzocrea@si.deis.unical.it Antwort an: Alfredo Cuzzocrea cuzzocrea@si.deis.unical.it An: AISWORLD Information Systems World Network isworld@lyris.isworld.org
International Journal of Business Intelligence and Data Mining
Special Issue on �OLAP Intelligence: Meaningfully Coupling OLAP and Data Mining Tools and Algorithms�
http://si.deis.unical.it/~cuzzocrea/IJBIDM2008/
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.
Important Dates ---------------
Abstract submission: May 1, 2008 Paper submission: May 15, 2008 Paper Acceptance/Rejection Notification - First Round: July 1, 2008 Revised Paper Submission: August 1, 2008 Final Paper Acceptance/Rejection Notification: August 15, 2008 Camera-Ready Versions on Accepted Papers Submission: September 5, 2008 IJBIDM Special Issue Publication: December 2008
Submission Guidelines and Instructions --------------------------------------
Submitted papers should not be currently under consideration for publication elsewhere. Submission process includes abstract and paper submission.
Abstracts (deadline May 1, 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 May 15, 2008) should be submitted in PDF or Postscript format using the Online Submissions of Papers (http://www.inderscience.com/mapper.php?id=35&jid=143). 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 (http://www.inderscience.com/mapper.php?id=31) Web page.
Program Committee Chair -----------------------
Alfredo Cuzzocrea (http://si.deis.unical.it/~cuzzocrea/) � ICAR Institute and University of Calabria, Italy
Program Committee -----------------
Alberto Abello (http://www.lsi.upc.edu/~aabello/), Polytechnical University of Catalunya, Spain Yuan An (http://www.ischool.drexel.edu/faculty/yan/), Drexel University, PA, USA Antonio Badia (http://date.spd.louisville.edu/badia/), University of Louisville, KY, USA Ladjel Bellatreche (http://www.lisi.ensma.fr/members/bellatreche/), LISI Laboratory, ENSMA, France Jerome Darmont (http://eric.univ-lyon2.fr/~jdarmont/?lang=eng), ERIC Laboratory, University Lumière Lyon 2, France Karen C. Davis (http://www.ece.uc.edu/~kcd/), University of Cincinnati, OH, USA Todd Eavis (http://users.encs.concordia.ca/~eavis/), Concordia University, Canada Joseph Fong (http://www.cs.cityu.edu.hk/~jfong/homepage/), City University of Hong Kong, China Pedro Furtado (http://eden.dei.uc.pt/~pnf/), University of Coimbra, Portugal Matteo Golfarelli (http://bias.csr.unibo.it/golfarelli/), University of Bologna, Italy Carlos Hurtado (http://www.dcc.uchile.cl/~churtado/eindex.html), University of Chile, Chile Jens Lechtenborger (http://dbms.uni-muenster.de/people/Lechtenboerger/), University of Munster, Germany Jason Li (http://www.ischool.drexel.edu/faculty/jli/), Drexel University, PA, USA Pat Martin (http://www.cs.queensu.ca/home/martin/), Queen's University, Ontario, Canada Rokia Missaoui (http://w3.uqo.ca/missaoui/), University of Quebec, Quebec, Canada Muhesh Mohania (http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/m/Mohania:Mukesh_K...), IBM India Research Lab, India Mirek Riedewald (http://www.cs.cornell.edu/~mirek/), Cornell University, NY, USA Timos Sellis (http://www.dblab.ece.ntua.gr/~timos/), National Technical University of Athens, Greece Alkis Simitsis (http://www.dblab.ece.ntua.gr/~asimi/), Stanford University, CA, USA Igor Timko (http://aws.unibz.it/staff/staff_detail.asp?LanguageID=EN&type=coll&c...), Free University of Bozen-Bolzano, Italy Juan Trujillo (http://www.dlsi.ua.es/~jtrujillo/), University of Alicante, Spain Wei Wang (http://www.cse.unsw.edu.au/~weiw/), University of New South Wales, Australia Robert Wrembel (http://www.cs.put.poznan.pl/rwrembel/), Poznan University of Technology, Poland
For more information and any inquire, please contact Alfredo Cuzzocrea at cuzzocrea@si.deis.unical.it
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