Historic Moments Discovery in Sequence Data

Abstract

Many emerging applications are based on finding interesting subsequences from sequence data. Finding “prominent streaks,” a set of the longest contiguous subsequences with values all above (or below) a certain threshold, from sequence data is one of that kind that receives much attention. Motivated from real applications, we observe that prominent streaks alone are not insightful enough but require the discovery of something we coined as “historic moments” as companions. In this article, we present an algorithm to efficiently compute historic moments from sequence data. The algorithm is incremental and space optimal , meaning that when facing new data arrival, it is able to efficiently refresh the results by keeping minimal information. Case studies show that historic moments can significantly improve the insights offered by prominent streaks alone. Furthermore, experiments show that our algorithm can outperform the baseline in both time and space.