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Model-based search in large time series databases

Published: 25 May 2011 Publication History

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.

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Cited By

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  • (2014)Model-Based Time Series ClassificationAdvances in Intelligent Data Analysis XIII10.1007/978-3-319-12571-8_16(179-191)Online publication date: 2014
  • (2012)Fast variable selection for memetracker phrases time series predictionProceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2413097.2413156(1-6)Online publication date: 6-Jun-2012
  • (2012)A Machine-Learning Classification Approach to Automatic Detection of Workers' Actions for Behavior-Based Safety AnalysisComputing in Civil Engineering (2012)10.1061/9780784412343.0009(65-72)Online publication date: 19-Sep-2012

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cover image ACM Other conferences
PETRA '11: Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
May 2011
401 pages
ISBN:9781450307727
DOI:10.1145/2141622
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • NSF: National Science Foundation
  • Foundation of the Hellenic World
  • ICS-FORTH: Institute of Computer Science, Foundation for Research and Technology - Hellas
  • U of Tex at Arlington: U of Tex at Arlington
  • UCG: University of Central Greece
  • Didaskaleio Konstantinos Karatheodoris, University of the Aegean
  • Fulbrigh, Greece: Fulbright Foundation, Greece
  • Ionian: Ionian University, GREECE

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 May 2011

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Author Tags

  1. dynamic time warping
  2. hidden Markov models
  3. time series

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  • Research-article

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PETRA '11
Sponsor:
  • NSF
  • ICS-FORTH
  • U of Tex at Arlington
  • UCG
  • Fulbrigh, Greece
  • Ionian

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Cited By

View all
  • (2014)Model-Based Time Series ClassificationAdvances in Intelligent Data Analysis XIII10.1007/978-3-319-12571-8_16(179-191)Online publication date: 2014
  • (2012)Fast variable selection for memetracker phrases time series predictionProceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2413097.2413156(1-6)Online publication date: 6-Jun-2012
  • (2012)A Machine-Learning Classification Approach to Automatic Detection of Workers' Actions for Behavior-Based Safety AnalysisComputing in Civil Engineering (2012)10.1061/9780784412343.0009(65-72)Online publication date: 19-Sep-2012

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