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Technological advances have created an unprecedented availability of inexpensive sensors capable of streaming environmental data in real-time. Data stream engines (DSE) with tuple processing rates of around 500k tuples/s have demonstrated their ability to both keep up with large numbers of spatio-temporal data streams, and execute stream window queries over them efficiently. Typically, geographically distributed sensors take samples asynchronously; however, when approximating the reality of a continuous phenomenon --- such as the radiation field over an urban region- the objective is to integrate their values correctly over space as well as over time. This paper presents an approach to extend DSEs with support enabling sliding window queries over dynamic continuous phenomena, which return both spatio-temporal snapshot and movies as window query results. We introduce a novel grid-pane index as a main memory index structure shared between multi-queries over a phenomenon and an adaptive, data driven kNN algorithm for efficiently approximating cells based on available stream data samples. AkNN implements a spatio-temporal inverse distance weighting interpolation (IDW) method that integrates time with space via an anisotropic ratio. Further, we introduce the shell list template that allows quick calculation of NN cells by distance in a space-time (ST) cuboid. We performed extensive performance evaluations using the Fukushima nuclear event in March 2011 as a test data set.
Whittier et al. (Tue,) studied this question.