Efficiently detecting clusters of mobile objects in the presence of dense noise
Proceedings of the 2010 ACM Symposium on Applied Computing
Interval-orientation patterns in spatio-temporal databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Mining pixel evolutions in satellite image time series for agricultural monitoring
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Extracting urban patterns from location-based social networks
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
The pattern next door: towards spatio-sequential pattern discovery
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Exploring multivariate spatio-temporal change in climate data using image analysis techniques
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Trajectory mining from anonymous binary motion sensors in Smart Environment
Knowledge-Based Systems
Cross-Correlation Measure for Mining Spatio-Temporal Patterns
Journal of Database Management
A novel real-time framework for extracting patterns from trajectory data streams
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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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 spatio-temporal sequential patterns are crucial to investigate spatial and temporal evolutions of phenomena in many application domains. Recent literatures have explored the sequential patterns on transaction data and trajectory analysis on moving objects. However, these methods can not be directly applied to mining sequential patterns from a large number of spatio-temporal events. Two major research challenges are still remaining: (i) the definition of significance measures for spatio-temporal sequential patterns to avoid spurious ones; (ii) 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 challenges 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 datasets shows that the Slicing-STS-Miner is an order of magnitude faster than STS-Miner for large datasets.