On efficient matching of spatiotemporal rules

Spatiotemporal rules are ubiquitous nowadays. For example, a user may need to pay for a phone call or the usage of the Internet services. The payment may depend on many factors such as the duration of the call, the locations of the caller and callee, etc. Thus, the rules are spatiotemporal in nature. Given a set of such spatiotemporal rules and the behavior of a user, the problem of spatiotemporal rule matching (STRM) is to find the activated rules for the behavior. In this paper, we consider user behaviors as special instances of spatiotemporal data and formalize the STRM problem. We show how to represent common rules as spatiotemporal rules. Then, we show how to process multiple rules in advance to generate non-conflicting rules and organize them into tree-based index structures. After that, we propose novel algorithms, taking advantage of the tree-based index structures, to solve the STRM problem. Extensive experiments show that our proposed algorithms can outperform the baseline methods by a large margin, and thus are applicable in large scale real-life scenarios.