Abstract. 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 a Breadth-First-Search-like approach and are not efficient for discovering long frequent sequences. Moreover, they require storing in main memory all occurrences of each sequence in the database and, as a result, the amount of space needed is rather large. Experiments show that the I/O cost of the database scans is offset by the efficiency of the DFS-like approach that ensures fast discovery of long frequent patterns. DFS_MINE is also ideal for mining frequent spatiotemporal sequences with various spatial granularities. Spatial granularity refers to how fine or how general our view of the space we are examining is.
DFS-MINE REF reports only maximal frequent sequential patterns and employs a bottom-up, depth-first search strategy.
Efficient mining of spatiotemporal patterns
In Proc. of Symposium on Advances in Spatial and Temporal Databases
Computer Science