Finding Sequential Patterns from Massive Number of Spatio-Temporal Events
Yan Qun Huang, Liqin Zhang, Pusheng Zhang · 2006
Given a large spatio-temporal database of events, where each event consists of the following fields: event-ID, time, location, event-type, mining spatio-temporal sequential patterns is to identify significant event type sequences. Such spatiotemporal sequential patterns are crucial to investigate spatial and temporal evolutions of phenomena in many application domains. In this paper, we propose a sequence index as the significance measure for spatio-temporal sequential patterns, which is meaningful due to its interpretability using spatial statistics. We propose two algorithms, namely STSMiner and Slicing-STS-Miner, to tackle the algorithmic design challenges under the spatial sequence index which does not preserve the downward closure property. We evaluate the algorithms by experimentally conducting performance evaluations using both synthetic and real world datasets.