Data management and data mining in spatio-temporal databases

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Spatio-temporal data is being collected at an increasing rate, e.g., through satellite images, GPS systems, mobile communication networks, medical imaging technology, sensor networks, or video observations etc. In this proposal, we consider spatio-temporal data in its "raw" form to be data that is (partially) described by locations and/or extensions of objects in a spatial frame of reference over time. Spatial frames of reference can be, for instance, parts of the earth's surface, a city's street network, a model of the brain of humans, a building, parts of an underwater area explored by a remotely operated vehicle, etc. Spatio-temporal data often represents moving objects (e.g., people moving around with a GPS enabled device) or moving and evolving geometries (e.g., forest fires). The increased availability of this type of data presents a great opportunity for advanced services and data analyses in application areas such as mobile computing and commerce, fleet control, monitoring and analyzing of environmental and socioeconomic phenomena, traffic control, animal tracking, radiation treatment planning for tumours, epidemiological analyses, etc. However, advanced data analysis and querying techniques for large volumes of spatio-temporal data are still in its early stages. On the one hand, geographic information systems (GIS), although they provide sophisticated analysis and display methods for spatial data, are not yet ready to deal with large data volumes and the complexity of exploratory spatio-temporal analysis. On the other hand, object-relational database systems, which provide efficient access methods for non-spatial and to some degree spatial data, do not yet support spatio-temporal data in an effective and efficient way (although research in recent years has advanced significantly in the area of modeling and indexing trajectory data for basic query processing). The global objective of this research program is to bridge this gap, and to devise general database technology to support a wide range of advanced applications, analyses, and services by developing effective and efficient methods and algorithms for representing, storing, querying, and mining of spatio-temporal data at different scales.