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A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
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@MISC{Huang_aframework,
author = {Yan Huang and Liqin Zhang and Pusheng Zhang},
title = {A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets},
year = {}
}
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Abstract
Abstract—Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns identifies significant event-type sequences. Such spatio-temporal sequential patterns are crucial to the investigation of spatial and temporal evolutions of phenomena in many application domains. Recent research literature has explored the sequential patterns on transaction data and trajectory analysis on moving objects. However, these methods cannot be directly applied to mining sequential patterns from a large number of spatio-temporal events. Two major research challenges still remain: 1) the definition of significance measures for spatio-temporal sequential patterns to avoid spurious ones and 2) the algorithmic design under the significance measures, which may not guarantee the downward closure property. 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 a novel algorithm called Slicing-STS-Miner to tackle the algorithmic design challenge using the spatial sequence index, which does not preserve the downward closure property. We compare the proposed algorithm with a simple algorithm called STS-Miner that utilizes the weak monotone property of the sequence index. Performance evaluations using both synthetic and real-world data sets show that the Slicing-STS-Miner is an order of magnitude faster than STS-Miner for large data sets. Index Terms—Spatio-temporal sequential pattern, density ratio, sequence index, slicing, performance. Ç 1
Keyphrases
spatio-temporal event data set sequence index significance measure spatio-temporal sequential pattern mining sequential pattern downward closure property sequential pattern large data set abstract given trajectory analysis spurious one performance evaluation novel algorithm spatial statistic transaction data algorithmic design challenge many application domain density ratio field event id simple algorithm spatial sequence index weak monotone property index term spatio-temporal sequential pattern large number temporal evolution event type spatio-temporal event algorithmic design major research challenge recent research literature large spatio-temporal database real-world data set
