A Greedy Approach Towards Parsimonious Temporal Aggregation

Abstract

Temporal aggregation is a crucial operator in temporal databases and has been studied in various flavors. In instant temporal aggregation (ITA) the aggregate value at time instant t is computed from the tuples that hold at t. ITA considers the distribution of the input data and works at the smallest time granularity, but the result size depends on the input timestamps and can get twice as large as the input relation. In span temporal aggregation (STA) the user specifies the timestamps over which the aggregates are computed and thus controls the result size. In this paper we introduce a new temporal aggregation operator, called greedy parsimonious temporal aggregation (PTAg), which combines features from ITA and STA. The operator extends and approximates ITA by greedily merging adjacent tuples with similar aggregate values until the number of result tuples is sufficiently small, which can be controlled by the application. Thus, PTAg considers the distribution of the data and allows to control the result size. Our empirical evaluation on real world data shows good results: considerable reductions of the result size introduce small errors only.