NDLI: Model-based search in large time series databases

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Model-based search in large time series databases

Content Provider	ACM Digital Library
Author	Athitsos, Vassilis Hollmén, Jaakko Papapetrou, Panagiotis Kotsifakos, Alexios Gunopulos, Dimitrios
Abstract	An important theoretical topic in assistive environments is reasoning about temporal patterns, that represent the sequential output of various sensors, and that can give us information about the health and activities of humans and the state of the environment. The recent growth in the quantity and quality of sensors for assistive environments has made it possible to create large databases of temporal patterns, that store sequences of observations obtained from such sensors over large time intervals. A topic of significant interest is being able to search such large databases so as to identify content of interest, for example activities of a certain type, or information about a patient's well-being. In this paper, we study two different approaches for conducting such searches: an exemplar-based approach, where we describe what we are looking for by giving an example, and a model-based approach, where we describe what we are looking for via a generative model. In particular, we describe the two different approaches, and we identify some important pros and cons for each approach. We also perform a comparative evaluation of exemplar-based search using dynamic time warping (DTW), and model-based search using Hidden Markov Models (HMMs), on large real datasets. In our experiments, when the number of training objects per model is sufficiently high, model-based search using HMMs produces more accurate search results than exemplar-based search using DTW.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450307727
DOI	10.1145/2141622.2141666
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dynamic time warping Hidden markov models Time series
Content Type	Text
Resource Type	Article

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