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Authors: Boyan Kolev 1 ; Reza Akbarinia 1 ; Ricardo Jimenez-Peris 2 ; Oleksandra Levchenko 1 ; Florent Masseglia 1 ; Marta Patino 3 and Patrick Valduriez 1

Affiliations: 1 Inria and LIRMM, Montpellier and France ; 2 LeanXcale, Madrid and Spain ; 3 Universidad Politecnica de Madrid (UPM), Madrid and Spain

Keyword(s): Time Series Correlation, Data Stream Processing, Distributed Computing.

Abstract: This paper addresses the problem of continuously finding highly correlated pairs of time series over the most recent time window. The solution builds upon the ParCorr parallel method for online correlation discovery and is designed to run continuously on top of the UPM-CEP data streaming engine through efficient streaming operators. The implementation takes advantage of the flexible API of the streaming engine that provides low level primitives for developing custom operators. Thus, each operator is implemented to process incoming tuples on-the-fly and hence emit resulting tuples as early as possible. This guarantees a real pipelined flow of data that allows for outputting early results, as the experimental evaluation shows.

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Paper citation in several formats:
Kolev, B. ; Akbarinia, R. ; Jimenez-Peris, R. ; Levchenko, O. ; Masseglia, F. ; Patino, M. and Valduriez, P. (2019). Pipelined Implementation of a Parallel Streaming Method for Time Series Correlation Discovery on Sliding Windows. In Proceedings of the 8th International Conference on Data Science, Technology and Applications - ADITCA; ISBN 978-989-758-377-3; ISSN 2184-285X, SciTePress, pages 431-436. DOI: 10.5220/0008191304310436

@conference{aditca19,
author={Boyan Kolev and Reza Akbarinia and Ricardo Jimenez{-}Peris and Oleksandra Levchenko and Florent Masseglia and Marta Patino and Patrick Valduriez},
title={Pipelined Implementation of a Parallel Streaming Method for Time Series Correlation Discovery on Sliding Windows},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - ADITCA},
year={2019},
pages={431-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008191304310436},
isbn={978-989-758-377-3},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - ADITCA
TI - Pipelined Implementation of a Parallel Streaming Method for Time Series Correlation Discovery on Sliding Windows
SN - 978-989-758-377-3
IS - 2184-285X
AU - Kolev, B.
AU - Akbarinia, R.
AU - Jimenez-Peris, R.
AU - Levchenko, O.
AU - Masseglia, F.
AU - Patino, M.
AU - Valduriez, P.
PY - 2019
SP - 431
EP - 436
DO - 10.5220/0008191304310436
PB - SciTePress

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