In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper p...

Space and time are implicit in every activity of life. Every real-world object has its past, present, future and hence is intrinsically tied up with location and time. Storing spatio-temporal attributes in the databases along with the thematic attributes enriches the data and the inherent knowledge stored in the database. Spatio-temporal databases provide description of real-world phenomenon in...

ÐThis paper investigates temporal changes of topological relationships and thereby integrates two important research areas: First, two-dimensional topological relationships that have been investigated quite intensively and, second, the change of spatial information over time. We investigate spatio-temporal predicates, which describe developments of well-known spatial topological relationships. ...

Spatio-temporal patterns extracted from historical trajectories of moving objects unveil important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of regional symbols and discover frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations in the original dat...

The goal of data mining is to discover nuggets. Spatial data mining discovers collocation rules. Especially in spatial data mining, when spatial data is relatively represented with time series, a spatio-temporal significance is inferred. In this context the collocation rule that is a quintessence for the spatial data, obtains changes to its size and shape with temporal influence. Thus, the chan...

In the geometric data model for spatio-temporal data, introduced by Chomicki and Revesz [1], spatio-temporal data are modelled as a finite collection of triangles that are transformed by time-dependent affinities of the plane. To facilitate querying and animation of spatio-temporal data, we present a normal form for data in the geometric data model. We propose an algorithm for constructing this...

Spatio-temporal data analysis plays an important role in many scientific applications like environmental epidemiology and public health. Our approach STARS (SpatioTemporal dAtabase system Realized with Shore) integrates spatio-temporal aspects in the persistent object system SHORE [1]. STARS provides an object oriented query language to formulate spatio-temporal queries which can be processed b...

There exist numerous indexing methods which handle either spatio-temporal or high-dimensional data well. However, those indexing methods which handle spatio-temporal data well have certain drawbacks when confronted with high-dimensional data. As the most efficient spatio-temporal indexing methods are based on the R-tree and its variants, they face the well known problems in high-dimensional spa...