Causality Join Query Processing for Data Streams via a Spatiotemporal Sliding Window

Data streams collected from sensors contain a large volume of useful information including causal relationships. Causality join query processing involves retrieving a set of pairs (cause, effect) from streams of data. However, some causal pairs may be omitted from the query result, due to the delay between sensors and the data stream management system, and the limited size of the sliding window. In this paper, we first investigate temporal, spatial, and spatiotemporal aspects of causality join query processing for data streams. Second, we propose several strategies for sliding window management based on these results. The accuracy of the proposed strategies is studied via intensive experimentation. The result shows that we can improve the accuracy of causality join query processing in data streams with respect to the simple FIFO strategy.