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 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 | |
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