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Authors: Corinna Giebler ; Christoph Stach ; Holger Schwarz and Bernhard Mitschang

Affiliation: Institute for Parallel and Distributed Systems, University of Stuttgart, Universitätsstraße 38, D-70569 Stuttgart and Germany

ISBN: 978-989-758-318-6

Keyword(s): Big Data, IoT, Batch Processing, Stream Processing, Lambda Architecture, Kappa Architecture.

Abstract: The Internet of Things is applied in many domains and collects vast amounts of data. This data provides access to a lot of knowledge when analyzed comprehensively. However, advanced analysis techniques such as predictive or prescriptive analytics require access to both, history data, i. e., long-term persisted data, and real-time data as well as a joint view on both types of data. State-of-the-art hybrid processing architectures for big data—namely, the Lambda and the Kappa Architecture—support the processing of history data and real-time data. However, they lack of a tight coupling of the two processing modes. That is, the user has to do a lot of work manually in order to enable a comprehensive analysis of the data. For instance, the user has to combine the results of both processing modes or apply knowledge from one processing mode to the other. Therefore, we introduce a novel hybrid processing architecture for big data, called BRAID. BRAID intertwines the processing of history data and real-time data by adding communication channels between the batch engine and the stream engine. This enables to carry out comprehensive analyses automatically at a reasonable overhead. (More)

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Paper citation in several formats:
Giebler, C.; Stach, C.; Schwarz, H. and Mitschang, B. (2018). BRAID - A Hybrid Processing Architecture for Big Data.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 294-301. DOI: 10.5220/0006861802940301

@conference{data18,
author={Corinna Giebler. and Christoph Stach. and Holger Schwarz. and Bernhard Mitschang.},
title={BRAID - A Hybrid Processing Architecture for Big Data},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={294-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006861802940301},
isbn={978-989-758-318-6},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - BRAID - A Hybrid Processing Architecture for Big Data
SN - 978-989-758-318-6
AU - Giebler, C.
AU - Stach, C.
AU - Schwarz, H.
AU - Mitschang, B.
PY - 2018
SP - 294
EP - 301
DO - 10.5220/0006861802940301

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