Finding Orientation-Sensitive Patterns in Snapshot Databases

Snapshot data have become ubiquitous, e.g., maps, images and videos. By extracting interesting features from snapshot data and analyzing their relative orientations and proximities, we can discover important structure configuration information among groups of features in a snapshot database. In this paper, we introduce a class of pattern called orientation-sensitive patterns, which occur in many applications ranging from weather study, sport game analysis to medical image processing. We examine three approaches to discover orientation-sensitive patterns. We show that the first apriori-based approach is expensive while the second enumeration-based approach is memory intensive. The third approach decomposes an orientation- sensitive pattern into an H-list and a V-list, which greatly simplifies the mining process. Extensive experiment studies show that the third method is more efficient and scalable than the apriori and enumeration algorithms. We also present case studies on soccer game snapshots to demonstrate the interesting patterns discovered.