A conceptual view on trajectories
Data & Knowledge Engineering
Building real-world trajectory warehouses
Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Similarity in (spatial, temporal and) spatio-temporal datasets
Proceedings of the 15th International Conference on Extending Database Technology
Spatio-temporal aggregations in trajectory data warehouses
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Materialized views for count aggregates of spatial data
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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In this papser we discuss how data warehousing technology can be used to store aggregate information about trajectories and perform OLAP operations over them. To this end, we define a data cube with spatial and temporal dimensions, discretized according to a regular grid. We investigate in depth some issues related to the computation of a holistic aggregate function, i.e, the presence, which returns the number of distinct trajectories occurring in a given spatio-temporal area. In particular, we introduce a novel way to compute an approximate, but nevertheless very accurate, presence aggregate function, which uses only a bounded amount of measures stored in the base cells of our cuboid. We also concentrate on the loading phase of our data warehouse, which has to deal with an unbounded stream of trajectory observations. We suggest how the complexity of this phase can be reduced, and we analyse the errors that this procedure induces at the level of the subaggregates stored in the base cells. These errors and the accuracy of our approximate aggregate functions are carefully evaluated by means of tests performed on synthetic trajectory datasets.