document: Prominent streak discovery 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 . For finding prominent streaks , we make the observation that prominent streaks are 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 a 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 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 .
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