Time Series Queries Processing with GPU Support

Krzysztof Kaczmarski , Piotr Przymus

Abstract

In recent years, an increased interest in processing and exploration of time-series has been observed. Due to the growing volumes of data, extensive studies have been conducted in order to find new and effective methods for storing and processing data. Research has been carried out in different directions, including hardware based solutions or NoSQL databases. We present a prototype query engine based on GPGPU and NoSQL database plus a new model of data storage using lightweight compression. Our solution improves the time series database performance in all aspects and after some modifications can be also extended to general-purpose databases in the future.
Author Krzysztof Kaczmarski (FMIS / DACSCM)
Krzysztof Kaczmarski,,
- Department of Applied Computer Science and Computation Methods
, Piotr Przymus - [Uniwersytet Mikołaja Kopernika w Toruniu]
Piotr Przymus,,
-
- Uniwersytet Mikołaja Kopernika w Toruniu
Pages53-60
Publication size in sheets0.5
Book Catania Babara, Cerquitelli Tania, Chiusano Silvia, Guerrini Giovanna, Kämpf Mirko, Kemper Alfons, Novikov Boris, Palpanas Themis, Pokorný Jaroslav, Vakali Athena (eds.): 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, DOI:10.1007/978-3-319-01863-8_6
Keywords in Englishtime series database, lightweight compression, data-intensive computations, GPU, CUDA
Abstract in PolishZaprezentowano system bazy danych dedykowany do wydajnego przetwarzania szeregów czasowych. wraz z algorytmami dla procesorów GPU. Osiągnięto unikalną wydajność przewyższającą wielokrotnie aktualnie działające systemy tego typu.
DOIDOI:10.1007/978-3-319-01863-8_6
URL http://link.springer.com/chapter/10.1007%2F978-3-319-01863-8_6
Languageen angielski
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 10.0, 11-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 11-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Publication indicators Scopus Citations = 2
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