FlowMiner: Finding Flow Patterns in Spatio-Temporal Databases

Authors:
Junmei Wang;Wynne Hsu;Mong Li Lee;Jason Wang
Affiliations:
National University of Singapore;National University of Singapore;National University of Singapore;New Jersey Institute of Technology
Venue:
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Year:
2004

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Abstract

The widespread use of spatio-temporal databases and applications have 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. In this paper, 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.