A Discovering General Prominent Streaks in Sequence Data

This paper studies the problem of prominent streak discovery in sequence data. Given a sequence of values, a prominent streak is a long consecutive subsequence consisting of only large (small) values, e.g., consecutive games of outstanding performance in sports, consecutive hours of heavy network traffic, consecutive days of frequent mentioning of a person in social media, and so on. Prominent streak discovery provides insightful data patterns for data analysis in many real-world applications and is an enabling technique for computational journalism. Given its real-world usefulness and complexity, the research on prominent streaks in sequence data opens a spectrum of challenging problems. A baseline approach to finding prominent streaks is a quadratic algorithm that exhaustively enumerates all possible streaks and performs pairwise streak dominance comparison. For more efficient methods, we make the observation that prominent streaks are in fact skyline points in two dimensions– streak interval length and minimum value in the interval. Our solution thus hinges upon the idea to separate the two steps in prominent streak discovery– candidate streak generation and skyline operation over candidate streaks. For candidate generation, we propose the concept of local prominent streak (LPS). We prove that prominent streaks are a subset of LPSs and the number of LPSs is less than the length of a data sequence, in comparison with the quadratic number of candidates produced by the brute-force baseline method. We develop efficient algorithms based on the concept of LPS. The non-linear LPS-based method (NLPS) considers a superset of LPSs as candidates, and the linear LPS-based method (LLPS) further guarantees to consider only LPSs. The proposed properties and algorithms are also extended for discovering general top-k, multi-sequence, and multi-dimensional prominent streaks. The results of experiments using multiple real datasets verified the effectiveness of the proposed methods and showed orders of magnitude performance improvement against the baseline method.