Mining asynchronous periodic patterns in time series data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Spatio-Temporal Aggregation Using Sketches
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Mining periodic patterns in spatio-temporal sequences at different time granularities
Intelligent Data Analysis
A Neural Based WSN Mote Trajectory Reconstruction for Mining Periodic Patterns
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New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports
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Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining pixel evolutions in satellite image time series for agricultural monitoring
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Mining temporal patterns in popularity of web items
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Mining group movement patterns
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Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Cross-Correlation Measure for Mining Spatio-Temporal Patterns
Journal of Database Management
Discovering periodic patterns of nodal encounters in mobile networks
Pervasive and Mobile Computing
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In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data could unveil important information to the data analyst. Existing approaches for discovering periodic patterns focus on symbol sequences. However, these methods cannot directly be applied to a spatiotemporal sequence because of the fuzziness of spatial locations in the sequence. In this paper, we define the problem of mining periodic patterns in spatiotemporal data and propose an effective and efficient algorithm for retrieving maximal periodic patterns. In addition, we study two interesting variants of the problem. The first is the retrieval of periodic patterns that are frequent only during a continuous subinterval of the whole history. The second problem is the discovery of periodic patterns, whose instances may be shifted or distorted. We demonstrate how our mining technique can be adapted for these variants. Finally, we present a comprehensive experimental evaluation, where we show the effectiveness and efficiency of the proposed techniques.