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 proceedingConference contribution

1 Scopus citations


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 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 languageEnglish (US)
Title of host publicationAdvances in Databases and Information Systems - 22nd European Conference, ADBIS 2018, Proceedings
EditorsAndras Benczur, Tomas Horvath, Bernhard Thalheim
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783319983974
StatePublished - 2018
Event22nd East-European Conference on Advances in Databases and Information Systems, ADBIS 2018 - Budapest, Hungary
Duration: Sep 2 2018Sep 5 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11019 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other22nd East-European Conference on Advances in Databases and Information Systems, ADBIS 2018

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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