Tools
ToolsJamil, 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.
Full text available as:
|
PDF
a33-jamil.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial. 550kB | |
|
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
Abstract
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 |
Downloads
Downloads per month over past year
| Repository Staff Only - |