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Category:   E-CFP
Subject:   ECML/PKDD Workshop on Mining Spatio-Temporal Data
From:   Gennady Adrienko
Email:   gennady.andrienko_(on)_ais.fraunhofer.de
Date received:   17 Jun 2005
Deadline:   25 Jul 2005
Start date:   03 Oct 2005

*** Please, excuse multiple cross-postings ********************************** Call for papers ECML/PKDD�05 Workshop on �Mining Spatio-Temporal Data� Porto, Monday 3rd October 2005 before the 16th European Conference on Machine Learning (ECML'05) 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'05) Workshop homepage: http://www.di.uniba.it/%7Emalerba/activities/mstd/ Technical description Spatio-temporal data mining is an emerging research area dedicated to the development and application of novel computational techniques for the analysis of large spatio-temporal databases. The main impulse to research in this subfield of data mining comes from the large amount of * spatial data made available by GIS, CAD, robotics and computer vision applications, computational biology, mobile computing applications; * temporal data obtained by registering events (e.g., telecommunication or web traffic data) and monitoring processes and workflows. Both the temporal and spatial dimensions add substantial complexity to data mining tasks. First of all, the spatial relations, both metric (such as distance) and non-metric (such as topology, direction, shape, etc.) and the temporal relations (such as before and after) are information bearing and therefore need to be considered in the data mining methods. Secondly, some spatial and temporal relations are implicitly defined, that is, they are not explicitly encoded in a database. These relations must be extracted from the data and there is a trade-off between precomputing them before the actual mining process starts (eager approach) and computing them on-the-fly when they are actually needed (lazy approach). Moreover, despite much formalization of space and time relations available in spatio-temporal reasoning, the extraction of spatial/temporal relations implicitly defined in the data introduces some degree of fuzziness that may have a large impact on the results of the data mining process. Thirdly, working at the level of stored data, that is, geometric representations (points, lines and regions) for spatial data or time stamps for temporal data, is often undesirable. For instance, urban planning researchers are interested in possible relations between two roads, which either cross each other, or run parallel, or can be confluent, independently of the fact that the two roads are represented by one or more tuples of a relational table of �lines� or �regions�. Therefore, complex transformations are required to describe the units of analysis at higher conceptual levels, where human-interpretable properties and relations are expressed. Fourthly, spatial resolution or temporal granularity can have direct impact on the strength of patterns that can be discovered in the datasets. Interesting patterns are more likely to be discovered at the lowest resolution/granularity level. On the other hand, large support is more likely to exist at higher levels. Fifthly, many rules of qualitative reasoning on spatial and temporal data (e.g., transitive properties for temporal relations after and before), as well as spatio-temporal ontologies, provide a valuable source of domain independent knowledge that should be taken into account when generating patterns. How to express these rules and how to integrate spatio-temporal reasoning mechanisms in data mining systems are still open problems. Additional research issues related to spatio-temporal data mining concern visualization of spatio-temporal patterns and phenomena, scalability of the methods, data structures used to represent and efficiently index spatio-temporal data. This workshop will focus on research (frameworks, theories, methodologies, algorithms) and practice (applications, tools and standards) of knowledge discovery from datasets containing explicit or implicit temporal, spatial or spatio-temporal information. The aim of this workshop is to bring together experts in the analysis of temporal and spatial data mining and knowledge discovery in temporal, spatial or spatio-temporal database systems, as well as knowledge engineers and domain experts from allied disciplines. Topics The workshop will address all topics of spatio-temporal data mining, including: ? Methods for mining temporal, spatial and spatio-temporal data ? Representation issues in temporal, spatial and spatio-temporal data ? Fuzzy logic and management of uncertainty in the context of spatio-temporal data mining ? Handling autocorrelation in spatial, temporal and spatio-temporal data ? Mining time series and trend analysis ? Discovery of temporal patterns ? Visualization support to spatio-temporal data mining methods ? Synergy of visual and computational approaches ? Empirical studies of performance, other scalability issues ? Database architectures for spatio-temporal data mining ? Time-aware queries ? Parallel and distributed sequence mining ? Integration of data mining in GIS ? Mining spatio-temporal patterns from unstructured documents ? Applications in various domains, including finance and commerce, telecommunications, environment, Earth observation and monitoring, urban planning, web/road traffic, bioinformatics (protein folding, genomics), etc. Workshop Structure and Attendance The workshop aims to be a highly communicative meeting place for researchers working on similar topics, but coming from different communities. In order to achieve these goals, the workshop will consist of two invited talks, followed by short presentations and longer discussions. A panel session will be organized as the closing event of the workshop. All workshop participants must also register for the main ECML/PKDD conference. Workshop attendance will be limited to registered participants. Submission Procedure Authors are invited to submit original research contributions or experience reports in English. We encourage submission of works presenting early stages of cutting-edge research and development. Submitted papers must be unpublished and substantially different from papers under review. The maximum length of papers is 12 pages. Papers should be sent electronically (postscript or pdf) not later than July 25th, 2005 to mstd_(on)_di.uniba.it, Subject: Submission to MSTD Papers will be selected on the basis of review of full paper contributions. Authors should make certain that the data mining techniques they describe deal with the special issues that are associated with spatio-temporal data. Notification of acceptance will be given by August 15th, 2005. Final camera-ready copies of accepted papers will be due by September 5th, 2005. The ECML/PKDD organizers intend to prepare a CD containing all the workshop proceedings. Hence, electronic submissions to the workshop are essential so that this could be carried out. A web-publication of the proceedings is also expected after the conference. In addition to the ECML/PKDD workshop proceedings, it is intended to publish a selection of accepted papers in a special issue of the Journal of Intelligent Information Systems. Style Guide There is a joint paper style for the proceedings of all ECML/PKDD workshops. Therefore, papers should be formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Authors� instructions and style files can be downloaded from http://www.springer.de/comp/lncs/authors.html. Workshop chairs Dr. Gennady Andrienko Fraunhofer Institut Autonome Intelligente Systeme (FhG AIS), Schloss Birlinghoven, Sankt-Augustin, D-53754, Germany gennady.andrienko_(on)_ais.fraunhofer.de Tel.: +49 2241 142486, Fax +49 2241 142072 Prof. Donato Malerba Dipartimento di Informatica, University of Bari via Orabona 4, Bari, I-70125, Italy malerba_(on)_di.uniba.it Tel/Fax: +39 080 5443269 Dr. Michael May Fraunhofer Institut Autonome Intelligente Systeme (FhG AIS), Schloss Birlinghoven, Sankt-Augustin, D-53754 Germany michael.may_(on)_ais.fraunhofer.de Tel:: +49-2241-142486, Fax +49-2241-142072 Dr. Maguelonne Teisseire Université Montpellier 2 � LIRMM � CNRS 161 rue Ada, 34392 Montpellier Cedex 5, France teisseire_(on)_lirmm.fr Tel.: +33 (0)467 418 653 - Fax +33 (0)467 418 500 Program Committee: * Natalia Andrienko, Fraunhofer Institute AIS, Germany * Mark Gahegan, Penn State University, USA * Fosca Gianotti, Pisa KDD-Lab, Italy * Menno-Jan Kraak, ITC, the Netherlands * Antonietta Lanza, University of Bari, Italy * Anne Laurent, Université Montpellier 2 � LIRMM � CNRS, France * Alan MacEachren, Penn State University, USA * Florent Masseglia, INRIA Sophia Antipolis, France * Jian Pei, University at Buffalo, The State University of New York, USA * Ben Shneiderman, HICL, University of Maryland, USA * Antony Unwin, Augsburg University, Germany * Monica Wachowicz, Waheningen University, the Netherlands Workshop homepage: http://www.di.uniba.it/%7Emalerba/activities/mstd/ ============================================================= _______________________________________________ Elsnet-list mailing list Elsnet-list_(on)_mailman.elsnet.org http://mailman.elsnet.org/mailman/listinfo/elsnet-list

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