Efficient Indexing for Spatiotemporal Applications

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Indexing spatiotemporal data is an important problem for many applications (global change, transportation, social and multimedia applications). The goal of this project is to provide efficient access methods for data whose geometry changes over time. Two time-varying spatial attributes are considered, the object position and extent. Based on the rate by which these spatial attributes change, the discrete and continuous spatiotemporal environments are identified. In the discrete environment, spatiotemporal data changes in discrete steps. Efficient ways to answer historical queries on any past state of such spatiotemporal data are examined. In particular, selection, neighbor, aggregate, join and similarity queries are addressed using a "partial persistence" methodology. In the continuous spatiotemporal environment, data changes continuously. Instead of keeping the data position/extent at discrete times (which would result in enormous update/storage requirements) the functions by which this data changes are stored. This introduces the novel problem of indexing functions. Using this approach, selection, neighbor and aggregation queries about future locations of moving objects in one and two dimensions are addressed. The methods used in this project are expected to achieve at least 30% improvement over traditional access methods. The applicability of the completed work reaches multiple settings, including Geographic Information Systems, multimedia databases and transportation systems. Dissemination of project findings is provided through the project's web page: http://www.cs.ucr.edu/~tsotras/spatio-temporal.html.