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Scalable online learning for flink: SOLMA library.

Jamil, W., Duong, N.C., Wang, W., Mansouri, C., Mohamad, S. and Bouchachia, A., 2018. Scalable online learning for flink: SOLMA library. In: ECSA '18: 12th European Conference on Software Architecture, 24-28 September 2018, Madrid, Spain.

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a33-jamil.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1145/3241403.3241438


Driven by the needs of Flink to expand the offline engine to a hybrid one, a new machine learning (ML) library, called SOLMA is proposed. This library aims to cover online learning algorithms for data streams. In this setting, data streams are processed sequentially example by example. SOLMA, which is under development, currently contains two classes of algorithms: (i) basic streaming routines such as online sampling, online PCA, online statistical moments and (ii) advanced online ML algorithms covering in particular classification, regression and drift/anomaly detection and handling. This paper briefly highlights the concepts underlying SOLMA.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:31449
Deposited By: Symplectic RT2
Deposited On:12 Nov 2018 15:45
Last Modified:14 Mar 2022 14:13


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