Spatio-Temporal Data Management: Spatio-temporal data has always existed, but is becoming more and more commonly available and usable nowadays, therefore generating demand for more effective and efficient data management techniques. At a very high level, the task at hand is managing with who was where and when. A few sample application scenarios are as follows. Rental car companies can track their fleet using GPS devices installed in the vehicles in order to verify whether the rental contract was violated. Cell phones can also be equipped with GPS devices which, for instance, would facilitate the location of the person carrying it when an emergency call is placed and/or provide location-based services. Animals behaviour (or changes thereof) can also be seen as spatio-temporal data. In particular, one could correlate changes of behaviour to changes in the environment. Another application could be to proactively advise drivers of current road accidents based on their usual driving routine.
Data Management in Sensor Networks: A close and relatively new research topic related to spatio-temporal data is that of data management on (ad-hoc) wireless sensor networks. For instance, using this paradigm, (very small) sensors can be spread over a large area (e.g., a forest) in order to gather and store data which can be used for (a posteriori) query processing. A chief concern in this environment is to minimize the energy consumption during the network's lifetime, in particular during query processing time. Numerous issues, previously researched under the umbrella of distributed databases and stream processing, require new research within this new framework. A typically neglected "interface" area in this domain is that of data communication/networks and all such issues become even more complex when nodes are mobile.