Efficient Mining of Spatiotemporal Patterns

The problem of mining spatiotemporal patterns is finding sequences of events that occur frequently in spatiotemporal datasets. Spatiotemporal datasets store the evolution of objects over time. Examples include sequences of sensor images of a geographical region, data that describes the location and movement of individual objects over time, or data that describes the evolution of natural phenomena, such as forest coverage. The discovered patterns are sequences of events that occur most frequently. In this paper, we present DFS_MINE, a new algorithm for fast mining of frequent spatiotemporal patterns in environmental data. DFS_MINE, as its name suggests, uses a Depth-First-Search-like approach to the problem which allows very fast discoveries of long sequential patterns. DFS_MINE performs database scans to discover frequent sequences rather than relying on information stored in main memory, which has the advantage that the amount of space required is minimal. Previous approaches utilize ...

• Type: Conference
• Year: 2001
• Where: SSD
• Authors: Ilias Tsoukatos, Dimitrios Gunopulos
• Keywords: Database, Sequences, Spatiotemporal Datasets, Spatiotemporal Patterns, SSD 2001