Journal of Universal Computer Science
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, the authors first investigate temporal, spatial, and spatiotemporal aspects of causality join query processing for data streams. Second, they propose several strategies for sliding window management based on these results.