Mining Top-k Co-Occurrence Patterns across Multiple Streams (Extended Abstract)

We study the novel problem of continuous mining of top-k closed co-occurrence patterns across multiple streams. We employ sliding window setting in this problem, and each pattern is ranked based on count, which is the number of streams that have generated the pattern. Since objects are consecutively generated and deleted, the count of a given pattern is dynamic, which may change the rank of the pattern. This renders a challenge to monitoring the top-k answer in real-time. We propose an index-based algorithm that addresses the challenge and provides the exact answer. Specifically, we propose the CP-Graph, a hybrid index of graph and inverted file structures. The CP-Graph can efficiently compute the count of a given pattern and update the answer while pruning unnecessary patterns. Our experimental study on real datasets demonstrates the efficiency and scalability of our solution.