Location-Awareness in Time Series Compression

Xu Teng; Andreas Züfle; Goce P Trajcevski; Diego Klabjan

doi:10.1007/978-3-319-98398-1_6

Location-Awareness in Time Series Compression

Xu Teng^*, Andreas Züfle, Goce P Trajcevski, Diego Klabjan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

We present our initial findings regarding the problem of the impact that time series compression may have on similarity-queries, in the settings in which the elements of the dataset are accompanied with additional contexts. Broadly, the main objective of any data compression approach is to provide a more compact (i.e., smaller size) representation of a given original dataset. However, as has been observed in the large body of works on compression of spatial data, applying a particular algorithm “blindly” may yield outcomes that defy the intuitive expectations – e.g., distorting certainÂ topological relationships that exist in the “raw” data [7]. In this study, we quantify this distortion by defining a measure of similarity distortion based on Kendall’s T. We evaluate this measure, and the correspondingly achieved compression ratio for the five most commonly used time series compression algorithms and the three most common time series similarity measures. We report some of our observations here, along with the discussion of the possible broader impacts and the challenges that we plan to address in the future.

Original language	English (US)
Title of host publication	Advances in Databases and Information Systems - 22nd European Conference, ADBIS 2018, Proceedings
Editors	Andras Benczur, Tomas Horvath, Bernhard Thalheim
Publisher	Springer Verlag
Pages	82-95
Number of pages	14
ISBN (Print)	9783319983974
DOIs	https://doi.org/10.1007/978-3-319-98398-1_6
State	Published - 2018
Event	22nd East-European Conference on Advances in Databases and Information Systems, ADBIS 2018 - Budapest, Hungary Duration: Sep 2 2018 → Sep 5 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11019 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	22nd East-European Conference on Advances in Databases and Information Systems, ADBIS 2018
Country/Territory	Hungary
City	Budapest
Period	9/2/18 → 9/5/18

Funding

X. Teng—Research supported by NSF grant III 1823267. G. Trajcevski—Research supported by NSF grants III-1823279 and CNS-1823267, and ONR grant N00014-14-1-0215.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-98398-1_6

Cite this

Teng, X., Züfle, A., Trajcevski, G. P., & Klabjan, D. (2018). Location-Awareness in Time Series Compression. In A. Benczur, T. Horvath, & B. Thalheim (Eds.), Advances in Databases and Information Systems - 22nd European Conference, ADBIS 2018, Proceedings (pp. 82-95). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11019 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-98398-1_6

Teng, Xu ; Züfle, Andreas ; Trajcevski, Goce P et al. / Location-Awareness in Time Series Compression. Advances in Databases and Information Systems - 22nd European Conference, ADBIS 2018, Proceedings. editor / Andras Benczur ; Tomas Horvath ; Bernhard Thalheim. Springer Verlag, 2018. pp. 82-95 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Location-Awareness in Time Series Compression",

abstract = "We present our initial findings regarding the problem of the impact that time series compression may have on similarity-queries, in the settings in which the elements of the dataset are accompanied with additional contexts. Broadly, the main objective of any data compression approach is to provide a more compact (i.e., smaller size) representation of a given original dataset. However, as has been observed in the large body of works on compression of spatial data, applying a particular algorithm “blindly” may yield outcomes that defy the intuitive expectations – e.g., distorting certain{\^A} topological relationships that exist in the “raw” data [7]. In this study, we quantify this distortion by defining a measure of similarity distortion based on Kendall{\textquoteright}s T. We evaluate this measure, and the correspondingly achieved compression ratio for the five most commonly used time series compression algorithms and the three most common time series similarity measures. We report some of our observations here, along with the discussion of the possible broader impacts and the challenges that we plan to address in the future.",

author = "Xu Teng and Andreas Z{\"u}fle and Trajcevski, {Goce P} and Diego Klabjan",

note = "Funding Information: X. Teng—Research supported by NSF grant III 1823267. G. Trajcevski—Research supported by NSF grants III-1823279 and CNS-1823267, and ONR grant N00014-14-1-0215. Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 22nd East-European Conference on Advances in Databases and Information Systems, ADBIS 2018 ; Conference date: 02-09-2018 Through 05-09-2018",

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Teng, X, Züfle, A, Trajcevski, GP & Klabjan, D 2018, Location-Awareness in Time Series Compression. in A Benczur, T Horvath & B Thalheim (eds), Advances in Databases and Information Systems - 22nd European Conference, ADBIS 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11019 LNCS, Springer Verlag, pp. 82-95, 22nd East-European Conference on Advances in Databases and Information Systems, ADBIS 2018, Budapest, Hungary, 9/2/18. https://doi.org/10.1007/978-3-319-98398-1_6

Location-Awareness in Time Series Compression. / Teng, Xu; Züfle, Andreas; Trajcevski, Goce P et al.
Advances in Databases and Information Systems - 22nd European Conference, ADBIS 2018, Proceedings. ed. / Andras Benczur; Tomas Horvath; Bernhard Thalheim. Springer Verlag, 2018. p. 82-95 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11019 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Location-Awareness in Time Series Compression

AU - Teng, Xu

AU - Züfle, Andreas

AU - Trajcevski, Goce P

AU - Klabjan, Diego

N1 - Funding Information: X. Teng—Research supported by NSF grant III 1823267. G. Trajcevski—Research supported by NSF grants III-1823279 and CNS-1823267, and ONR grant N00014-14-1-0215. Publisher Copyright: © 2018, Springer Nature Switzerland AG.

PY - 2018

Y1 - 2018

N2 - We present our initial findings regarding the problem of the impact that time series compression may have on similarity-queries, in the settings in which the elements of the dataset are accompanied with additional contexts. Broadly, the main objective of any data compression approach is to provide a more compact (i.e., smaller size) representation of a given original dataset. However, as has been observed in the large body of works on compression of spatial data, applying a particular algorithm “blindly” may yield outcomes that defy the intuitive expectations – e.g., distorting certainÂ topological relationships that exist in the “raw” data [7]. In this study, we quantify this distortion by defining a measure of similarity distortion based on Kendall’s T. We evaluate this measure, and the correspondingly achieved compression ratio for the five most commonly used time series compression algorithms and the three most common time series similarity measures. We report some of our observations here, along with the discussion of the possible broader impacts and the challenges that we plan to address in the future.

AB - We present our initial findings regarding the problem of the impact that time series compression may have on similarity-queries, in the settings in which the elements of the dataset are accompanied with additional contexts. Broadly, the main objective of any data compression approach is to provide a more compact (i.e., smaller size) representation of a given original dataset. However, as has been observed in the large body of works on compression of spatial data, applying a particular algorithm “blindly” may yield outcomes that defy the intuitive expectations – e.g., distorting certainÂ topological relationships that exist in the “raw” data [7]. In this study, we quantify this distortion by defining a measure of similarity distortion based on Kendall’s T. We evaluate this measure, and the correspondingly achieved compression ratio for the five most commonly used time series compression algorithms and the three most common time series similarity measures. We report some of our observations here, along with the discussion of the possible broader impacts and the challenges that we plan to address in the future.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Teng X, Züfle A, Trajcevski GP, Klabjan D. Location-Awareness in Time Series Compression. In Benczur A, Horvath T, Thalheim B, editors, Advances in Databases and Information Systems - 22nd European Conference, ADBIS 2018, Proceedings. Springer Verlag. 2018. p. 82-95. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-98398-1_6

Location-Awareness in Time Series Compression

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