Dynamic Compression Strategy for Time Series Database Using GPU

Piotr Przymus , Krzysztof Kaczmarski

Abstract

Nowadays, we can observe increasing interest in processing and exploration of time series. Growing volumes of data and needs of efficient processing pushed research in new directions. GPU devices combined with fast compression and decompression algorithms open new horizons for data intensive systems. In this paper we present improved cascaded compression mechanism for time series databases build on Big Table–like solution. We achieved extremely fast compression methods with good compression ratio.
Author Piotr Przymus
Piotr Przymus,,
-
, Krzysztof Kaczmarski (FMIS / DACSCM)
Krzysztof Kaczmarski,,
- Department of Applied Computer Science and Computation Methods
Pages235-244
Publication size in sheets0.5
Book Catania Barbara, Cerquitelli Tania, Chiusano Silvia, Guerrini Giovanna, Kämpf Mirko, Kemper Alfons, Novikov Boris, Palpanas Themis, Pokorný Jaroslav, Vakali Athena: New Trends in Databases and Information Systems, 17th East European Conference on Advances in Databases and Information Systems, ADBIS 2013, Genoa, Italy, September 1-4, 2013. Proceedings II, Advances in Intelligent Systems and Computing, vol. 241, 2013, Springer, ISBN 978-3-319-01862-1
Keywords in Englishtime series database, lightweight lossless compression, GPU, CUDA
Abstract in PolishOpracowano nową technikę kompresji szeregów czasowych przy pomocy algorytmów lekkiej kompresji stosowanych wielokrotnie (kaskadowo). Plan kompresji opracowywany jest dynamicznie na podstawie charakterystyki danych wejściowych. Uzyskane wyniki pokazują, że taka kompresja jest bardzo wydajna. Jednocześnie uzyskane współczynniki kompresji są znacznie lepsze niż w klasycznych lekkich algorytmach.
DOIDOI:10.1007/978-3-319-01863-8_26
URL https://link.springer.com/chapter/10.1007/978-3-319-01863-8_26
Languageen angielski
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 10.0, 06-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 06-02-2020, BookChapterSeriesAndMatConfByConferenceseries
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