Dhont, M. , Tsiporkova, E., & Boeva, V. (2020). Layered Integration Approach for Multi-view Analysis of Temporal Data . In V. Lemaire, S. Malinowski, A. Bagnall, T. Guyet, R. Tavenard, & G. Ifrim (Eds.), Advanced Analytics and Learning on Temporal Data - 5th ECML PKDD Workshop, AALTD 2020, Revised Selected Papers: AALTD 2020: Advanced Analytics and Learning on Temporal Data (Vol. 12588, pp. 138-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12588 LNAI). Springer.

@inproceedings{d51af35bbf8444488bc9c37dbb9ea921,
title = " Layered Integration Approach for Multi-view Analysis of Temporal Data " ,
abstract = " In this study, we propose a novel data analysis approach that can be used for multi-view analysis and integration of heterogeneous temporal data originating from multiple sources. The proposed approach consists of several distinctive layers: (i) select a suitable set (view) of parameters in order to identify characteristic behaviour within each individual source (ii) exploit an alternative set (view) of raw parameters (or high-level features) to derive some complementary representations (e.g. related to source performance) of the results obtained in the first layer with the aim to facilitate comparison and mediation across the different sources (iii) integrate those representations in an appropriate way, allowing to trace back similar cross-source performance to certain characteristic behaviour of the individual sources.The validity and the potential of the proposed approach has been demonstrated on a real-world dataset of a fleet of wind turbines. " ,
keywords = " Data integration, Data mining, Temporal data clustering, Multi-view learning " ,
author = " Michiel Dhont and Elena Tsiporkova and Veselka Boeva " ,
note = " Funding Information: This research was subsidised by the Brussels-Capital Region - Innoviris, received funding from the Flemish Government (AI Research Program) and was supported by the Energy Transition Fund of the FPS Economy through the project BitWind. Funding Information: This research was subsidised by the Brussels-Capital Region-Innoviris, received funding from the Flemish Government (AI Research Program) and was supported by the Energy Transition Fund of the FPS Economy through the project BitWind. Publisher Copyright: { extcopyright} Springer Nature Switzerland AG 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved. null Conference date: 14-09-2020 Through 18-10-2020 " ,
year = " 2020 " ,
month = dec,
day = " 16 " ,
doi = " 10.1007/978-3-030-65742-0_10 " ,
language = " English " ,
isbn = " 978-3-030-65741-3 " ,
volume = " 12588 " ,
series = " Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) " ,
publisher = " Springer " ,
pages = " 138154 " ,
editor = " Vincent Lemaire and Simon Malinowski and Anthony Bagnall and Thomas Guyet and Romain Tavenard and Georgiana Ifrim " ,
booktitle = " Advanced Analytics and Learning on Temporal Data - 5th ECML PKDD Workshop, AALTD 2020, Revised Selected Papers " ,
url = " https://ecmlpkdd2020.net/ " ,
}