Workshop structure and attendance
Program Chairs and Commitee
ECML/PKDD Workshop on Mining Spatio-Temporal Data (MSTD)
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
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.