FlowMiner: Finding Flow Patterns in Spatio-Temporal Databases

Junmei Wang, Wynne Hsu, Mong-Li Lee, Jason Tsong-Li Wang

Abstract: The widespread use of spatio-temporal databases and applications has fuelled an urgent need to discover interesting time and space patterns in such databases. While much work has been done in discovering time/sequence patterns or spatial patterns, discovering of patterns involving both time and space dimensions is still in its infancy, We introduce the concept of flow patterns. Flow patterns are intended to describe the change of events over space and time. These flow patterns are useful to the understanding of many real-life applications. We present a disk-based algorithm, FlowMiner, which utilizes temporal relationships and spatial relationships amid events to generate flow patterns. Our performance study shows that FlowMiner is both scalable and efficient. Experiments on real-life datasets also reveal interesting flow patterns.