Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency

Abstract

Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or string of symbols, which can be seen as an input for various subsequence matching approaches. The variety of data types, specific tasks and their solutions is so wide that their proper comparison and combination suitable for a particular task might be very complicated and time-consuming. In this work, we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problem by a uniform frame that simplifies and speeds up the design, development and evaluation of subsequence matching related systems. We identify several relatively separate subtasks solved differently over the literature and SMF enables to combine them in a straightforward manner achieving new quality and efficiency. The strictly modular architecture and openness of SMF enables also involvement of efficient solutions from different fields, for instance advanced metric-based indexes