BerlinMOD: a benchmark for moving object databases
The VLDB Journal — The International Journal on Very Large Data Bases
Spatio-temporal aggregation of European air quality observations in the Sensor Web
Computers & Geosciences
Meaningful spatial prediction and aggregation
Environmental Modelling & Software
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Spatio-temporal databases are often associated withanalyses that summarize stored data over spatial, temporalor spatio-temporal dimensions. For example, a studyof traffic patterns might explore average traffic densitieson a road network at different times, over differentareas in space, and over different areas in space at differenttimes. The importance of temporal, spatial andspatio-temporal aggregation has been reflected in a significantnumber of proposals for algorithms for efficientcomputation of specific kinds of aggregation. However, althoughsuch proposals may be effective in particular cases,as yet there is no generic framework that provides efficientsupport for the wide range of partitioning and aggregationoperations that a spatio-temporal database managementsystem might be expected to support over bothstored and derived data. This paper proposes an algorithmicframework that can be applied to many differentforms of aggregation, and presents the results of performancestudies on an implementation of the framework.These show that the framework provides a scalable solutionfor the many cases in which the aggregationsrequired over stored and derived data may be widely variableand unpredictable.