research-article

Similarity matching for uncertain time series: analytical and experimental comparison

Authors:

Michele Dallachiesa,

Katsiaryna Mirylenka,

Themis PalpanasAuthors Info & Claims

QUeST '11: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data

Pages 8 - 15

https://doi.org/10.1145/2064969.2064971

Published: 01 November 2011 Publication History

Abstract

In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others.

Due to the inherent imprecision of sensor observations, many investigations have recently turned into querying, mining and storing uncertain data. Uncertainty can also be due to data aggregation, privacy-preserving transforms, and error-prone mining algorithms.

In this study, we survey the techniques that have been proposed specifically for modeling and processing uncertain time series, an important model for temporal data. We provide both an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach. We additionally conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results. Based on our evaluations, we also provide guidelines useful for practitioners in the field.

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Cited By

Saha SChattopadhyay S(2020)Exploring of the summer monsoon rainfall around the Himalayas in time domain through maximization of Shannon entropyTheoretical and Applied Climatology10.1007/s00704-020-03186-4Online publication date: 6-Apr-2020
https://doi.org/10.1007/s00704-020-03186-4
Mirylenka KScotton PMiksovic CAlaoui S(2020)Linking IT Product RecordsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-43887-6_9(101-111)Online publication date: 28-Mar-2020
https://doi.org/10.1007/978-3-030-43887-6_9
Mirylenka KDallachiesa MPalpanas TChoudhary AWu KDong B(2017)Data Series Similarity Using Correlation-Aware MeasuresProceedings of the 29th International Conference on Scientific and Statistical Database Management10.1145/3085504.3085515(1-12)Online publication date: 27-Jun-2017
https://dl.acm.org/doi/10.1145/3085504.3085515
Show More Cited By

Index Terms

Similarity matching for uncertain time series: analytical and experimental comparison
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

QUeST '11: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data

November 2011

42 pages

ISBN:9781450310376

DOI:10.1145/2064969

Copyright © 2011 ACM.

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]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2011

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SIGSPATIAL

GIS '11: 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 1, 2011

Illinois, Chicago

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Cited By

Saha SChattopadhyay S(2020)Exploring of the summer monsoon rainfall around the Himalayas in time domain through maximization of Shannon entropyTheoretical and Applied Climatology10.1007/s00704-020-03186-4Online publication date: 6-Apr-2020
https://doi.org/10.1007/s00704-020-03186-4
Mirylenka KScotton PMiksovic CAlaoui S(2020)Linking IT Product RecordsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-43887-6_9(101-111)Online publication date: 28-Mar-2020
https://doi.org/10.1007/978-3-030-43887-6_9
Mirylenka KDallachiesa MPalpanas TChoudhary AWu KDong B(2017)Data Series Similarity Using Correlation-Aware MeasuresProceedings of the 29th International Conference on Scientific and Statistical Database Management10.1145/3085504.3085515(1-12)Online publication date: 27-Jun-2017
https://dl.acm.org/doi/10.1145/3085504.3085515
Nabilah ROthman ZAzuraliza B(2016)Approaches of Handling Uncertain Time Series Data towards PredictionInternational Journal of Future Computer and Communication10.18178/ijfcc.2016.5.6.4775:6(233-236)Online publication date: 2016
https://doi.org/10.18178/ijfcc.2016.5.6.477
Radzuan NOthman ZBakar A(2013)Uncertain Time Series in Weather PredictionProcedia Technology10.1016/j.protcy.2013.12.22811(557-564)Online publication date: 2013
https://doi.org/10.1016/j.protcy.2013.12.228
Orang MShiri NChen XLebanon GWang HZaki M(2012)A probabilistic approach to correlation queries in uncertain time series dataProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398607(2229-2233)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398607
Marascu AKhan SPalpanas T(2012)Scalable similarity matching in streaming time seriesProceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II10.1007/978-3-642-30220-6_19(218-230)Online publication date: 29-May-2012
https://dl.acm.org/doi/10.1007/978-3-642-30220-6_19

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