PROJECT TITLE :

Non-Overlapping Subsequence Matching of Stream Synopses - 2018

ABSTRACT:

In this Project, we tend to propose SUbsequence Matching framework with cell MERgence (SUMMER) for online subsequence matching between histogram-primarily based stream synopsis structures below the dynamic time warping distance. Given a question synopsis pattern, SUMMER continuously identifies all the matching subsequences for a stream because the bins are generated. To effectively cut back the computation time, we style a Weighted Dynamic Time Warping (WDTW) algorithm, which computes the warping distance directly between two histogram-based mostly synopses. Furthermore, a Stack-based Overlapping Filter Algorithm (SOFA) is provided to remove the overlapping subsequences to avoid the redundant information. Finally, we have a tendency to design an optional refinement module to relax the subsequence range limit and improve the matching accuracy. Our experiments on real datasets show that the proposed method considerably hastens the pattern matching while not compromising the accuracy required when compared with different approaches.