Abstract:
This paper proposes a framework for the handling of spatio-temporal queries with inexact matches, using the concept of relation similarity. We initially describe a binary string encoding for 1D relations that permits the automatic derivation of similarity measures. We then extend this model to various granularity levels and many dimensions, and show that reasoning on spatio-temporal structure is significantly facilitated in the new framework. Finally, we provide algorithms and optimization methods for four types of queries: (i) object retrieval based on some spatio-temporal relations with respect to a reference object, (ii) spatial joins, i.e., retrieval of object pairs that satisfy some input relation, (iii) structural queries, which retrieve configurations matching a particular spatio-temporal structure, and (iv) special cases of motion queries. Considering the current large availability of multidimensional data and the increasing need for flexible query-answering mechanisms, our techniques can be used as the core of spatio-temporal query processors.
