[Elsnet-list] [Mlnet] CFP: ECML/PKDD Workshop on Mining Spatio-Temporal Data

Andrienko gennady.andrienko at ais.fraunhofer.de
Fri Jun 17 14:12:45 CEST 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/~malerba/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
 * spatial data made available by GIS, CAD, robotics and computer
vision applications, computational biology, mobile computing
 * temporal data obtained by registering events (e.g.,
telecommunication or web traffic data) and monitoring processes and

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.


The workshop will address all topics of spatio-temporal data mining,
?       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 at 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

Workshop chairs

Dr. Gennady Andrienko
Fraunhofer Institut Autonome Intelligente Systeme (FhG AIS),
Schloss Birlinghoven, Sankt-Augustin, D-53754, Germany
gennady.andrienko at 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 at 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 at 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 at 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/~malerba/activities/mstd/

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