Evaluating stream predicates over dynamic fields

Technological advances have created an unprecedented availability of inexpensive sensors able to stream environmental data in real-time. However, we still seek appropriate data management technology capable of handling this onslaught of sampling in previously unavailable spatial and temporal density. Data stream engines (DSEs) are state of the art data management tools that have update throughput rates of up to 500k tuples/s. In previous work we have shown that DSEs can be extended to generate smooth representations of continuous spatio-temporal fields sampled by up to 250K sensors on-the-fly in near real-time, creating a new representation every second. In this paper we investigate a spatio-temporal stream operator framework that can efficiently execute predicate operators over such spatio-temporal fields. Typical predicates are e.g. "find all sub-areas in a field that are below or above a certain threshold value". We present the requirements, the approach taken, and our results along with a performance evaluation.