Temporal aggregation in active database rules

An important feature of many advanced active database prototypes is support for rules triggered by complex patterns of events. Their composite event languages provide powerful primitives for event-based temporal reasoning. In fact, with one important exception, their expressive power matches and surpasses that of sophisticated languages offered by Time Series Management Systems (TSMS), which have been extensively used for temporal data analysis and knowledge discovery. This exception pertains to temporal aggregation, for which, current active database systems offer only minimal support, if any. In this paper, we introduce the language TREPL, which addresses this problem. The TREPL prototype, under development at UCLA, offers primitives for temporal aggregation that exceed the capabilities of state-of-the-art composite event languages, and are comparable to those of TSMS languages. TREPL also demonstrates a rigorous and general approach to the definition of composite event language semantics. The meaning of a TREPL rule is formally defined by mapping it into a set of Datalog 1 S rules, whose logic-based semantics characterizes the behavior of the original rule. This approach handles naturally temporal aggregates, including user-defined ones, and is also applicable to other composite event languages, such as ODE, Snoop and SAMOS.