Finding Maximal Periodic Patterns and Pruning Strategy in Spatiotemporal Databases

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. Periodic pattern mining or periodicity detection has numerous applications such as prediction, forecasting, detection of unusual events, etc. The periodic patterns are detected in a time-series database depending on the time intervals. existing approaches could not detect all types of periodicity such as symbol, sequence and segment at a time. In this paper, the authors propose an approach to detect all types of the periodicity in time series databases.

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