Efficient time-series subsequence matching using duality in constructing windows

In this paper, we propose a new subsequence matching method, . Dual Match exploits duality in constructing windows and significantly improves performance. Dual Match divides data sequences into disjoint windows and the query sequence into sliding windows, and thus, is a dual approach of the one by Faloutsos et al. (Proceedings of the ACM SIGMOD International Conference on Management of Data, Seattle, Washington, 1994, pp. 419–429.) ( in short), which divides data sequences into sliding windows and the query sequence into disjoint windows. FRM causes a lot of false alarms (i.e., candidates that do not qualify) by storing minimum bounding rectangles rather than individual points representing windows to save storage space for the index. Dual Match solves this problem by directly storing points without incurring excessive storage overhead. Experimental results show that, in most cases, Dual Match provides large improvement both in false alarms and performance over FRM given the same amount of storage space. In particular, for low selectivities (less than 10), Dual Match significantly improves performance up to 430-fold. On the other hand, for high selectivities (more than 10), it shows a very minor degradation (less than 29%). For selectivities in between (10–10), Dual Match shows performance slightly better than that of FRM. Overall, these results indicate that our approach provides a new paradigm in subsequence matching that improves performance significantly in large database applications.