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@misc{990776,
author = {Novák, David and Volný, Petr and Zezula, Pavel},
keywords = {subsequence matching; metric indexing; framework},
language = {eng},
publisher = {Cornell University Library},
title = {Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency},
url = {http://arxiv.org/abs/1206.2510v1},
year = {2012}
}
TY - GEN
ID - 990776
AU - Novák, David - Volný, Petr - Zezula, Pavel
PY - 2012
TI - Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency
VL - eprint arXiv:1206.2510
PB - Cornell University Library
KW - subsequence matching
KW - metric indexing
KW - framework
UR - http://arxiv.org/abs/1206.2510v1
L2 - http://arxiv.org/abs/1206.2510v1
N2 - 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 partial or full solutions is so wide that the choice, implementation and parametrization of a suitable solution for a given task might be complicated and time-consuming; a possibly fruitful combination of fragments from different research areas may not be obvious nor easy to realize. The leading authors of this field also mention the implementation bias that makes difficult a proper comparison of competing approaches. Therefore we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problems 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 straightforward manner achieving new quality and efficiency. This framework can be used in many application domains and its components can be reused effectively. Its strictly modular architecture and openness enables also involvement of efficient solutions from different fields, for instance efficient metric-based indexes. This is an extended version of a paper published on DEXA 2012.
ER -
NOVÁK, David, Petr VOLNÝ a Pavel ZEZULA. \textit{Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency}. Cornell University Library, 2012. eprint arXiv:1206.2510.
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