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Layered integration approach for multi-view analysis of temporal data
Sirris, BEL.
Sirris, BEL.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
2020 (English)In: Lecture Notes in Computer Science, Springer Science and Business Media Deutschland GmbH , 2020, Vol. 12588, p. 138-154Conference paper, Published paper (Refereed)
Abstract [en]

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. © Springer Nature Switzerland AG 2020.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2020. Vol. 12588, p. 138-154
Series
Lecture Notes in Computer Science , ISSN 0302-9743, E-ISSN 1611-3349 ; 12588
Keywords [en]
Data integration, Data mining, Multi-view learning, Temporal data clustering, Advanced Analytics, Analysis approach, Distinctive layers, High-level features, Integration approach, Multi-views, Multiple source, Temporal Data, Trace backs
National Category
Computer Sciences Signal Processing
Identifiers
URN: urn:nbn:se:bth-20902DOI: 10.1007/978-3-030-65742-0_10Scopus ID: 2-s2.0-85098272903ISBN: 9783030657413 (print)OAI: oai:DiVA.org:bth-20902DiVA, id: diva2:1516052
Conference
5th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2020, Ghent, Belgium, 18 September 2020 through 18 September 2020
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge FoundationAvailable from: 2021-01-11 Created: 2021-01-11 Last updated: 2022-05-06Bibliographically approved

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Boeva, Veselka

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