Layered Integration Approach for Multi-view Analysis of Temporal Data

Michiel Dhont; Elena Tsiporkova; Veselka Boeva

doi:10.1007/978-3-030-65742-0_10

Layered Integration Approach for Multi-view Analysis of Temporal Data

Michiel Dhont, Elena Tsiporkova, Veselka Boeva

Onderzoeksoutput: Conference paper › Research

6 Citaten (Scopus)

Samenvatting

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.

Originele taal-2	English
Titel	Advanced Analytics and Learning on Temporal Data - 5th ECML PKDD Workshop, AALTD 2020, Revised Selected Papers
Subtitel	AALTD 2020: Advanced Analytics and Learning on Temporal Data
Redacteuren	Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim
Uitgeverij	Springer
Pagina's	138-154
Aantal pagina's	17
Volume	12588
ISBN van elektronische versie	978-3-030-65742-0
ISBN van geprinte versie	978-3-030-65741-3
DOI's	https://doi.org/10.1007/978-3-030-65742-0_10
Status	Published - 16 dec 2020
Evenement	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020 - Online, Ghent, Belgium Duur: 14 sep 2020 → 18 okt 2020 https://ecmlpkdd2020.net/

Publicatie series

Naam	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12588 LNAI
ISSN van geprinte versie	0302-9743
ISSN van elektronische versie	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020
Verkorte titel	ECML PKDD
Land/Regio	Belgium
Stad	Ghent
Periode	14/09/20 → 18/10/20
Internet adres	https://ecmlpkdd2020.net/

Bibliografische nota

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:
© Springer Nature Switzerland AG 2020.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Toegang tot document

10.1007/978-3-030-65742-0_10

https://link.springer.com/chapter/10.1007%2F978-3-030-65742-0_10

Andere bestanden en links

Link to publication in Scopus

1 Afgelopen

BRGDOC9: Applied PhD 2019: MISTic: Trendanalyse met meerdere bronnen voor intelligente transportatie systemen
Munteanu, A. & Guns, T.
1/01/20 → 31/12/23
Project: Toegepast

6 Citaties
1 Article

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines
Dhont, M., Tsiporkova, E. & Boeva, V., okt 2021, In: Energies. 14, 19, 30 blz., 6216.
Onderzoeksoutput: Article › peer review

Open Access
6 Citaten (Scopus)

2 Talk or presentation at a workshop/seminar
1 Talk or presentation at a conference

Layered Integration Approach for Multi-view Analysis of Temporal Data
Michiel Dhont (Speaker)
18 sep 2020
Activiteit: Talk or presentation at a workshop/seminar
Layered Integration Approach for Multi-view Analysis of Temporal Data
Michiel Dhont (Speaker)
19 nov 2020
Activiteit: Talk or presentation at a conference
Multi-Layer Clustering Approach for Mining Multi-source Real-world Datasets
Michiel Dhont (Presenter)
19 feb 2020
Activiteit: Talk or presentation at a workshop/seminar

Citeer dit

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 (editors), 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, blz. 138-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12588 LNAI). Springer. https://doi.org/10.1007/978-3-030-65742-0_10

Dhont, Michiel ; Tsiporkova, Elena ; Boeva, Veselka. / Layered Integration Approach for Multi-view Analysis of Temporal Data. 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. redacteur / Vincent Lemaire ; Simon Malinowski ; Anthony Bagnall ; Thomas Guyet ; Romain Tavenard ; Georgiana Ifrim. Vol. 12588 Springer, 2020. blz. 138-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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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: {\textcopyright} Springer Nature Switzerland AG 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; null ; Conference date: 14-09-2020 Through 18-10-2020",

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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 (redactie), 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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12588 LNAI, Springer, blz. 138-154, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020, Ghent, Belgium, 14/09/20. https://doi.org/10.1007/978-3-030-65742-0_10

Layered Integration Approach for Multi-view Analysis of Temporal Data. / Dhont, Michiel; Tsiporkova, Elena; Boeva, Veselka.

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. redactie / Vincent Lemaire; Simon Malinowski; Anthony Bagnall; Thomas Guyet; Romain Tavenard; Georgiana Ifrim. Vol. 12588 Springer, 2020. blz. 138-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12588 LNAI).

Onderzoeksoutput: Conference paper › Research

TY - GEN

T1 - Layered Integration Approach for Multi-view Analysis of Temporal Data

AU - Dhont, Michiel

AU - Tsiporkova, Elena

AU - Boeva, Veselka

N1 - 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: © Springer Nature Switzerland AG 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/12/16

Y1 - 2020/12/16

N2 - 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.

AB - 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.

KW - Data integration

KW - Data mining

KW - Temporal data clustering

KW - Multi-view learning

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U2 - 10.1007/978-3-030-65742-0_10

DO - 10.1007/978-3-030-65742-0_10

M3 - Conference paper

SN - 978-3-030-65741-3

VL - 12588

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 138

EP - 154

BT - Advanced Analytics and Learning on Temporal Data - 5th ECML PKDD Workshop, AALTD 2020, Revised Selected Papers

A2 - Lemaire, Vincent

A2 - Malinowski, Simon

A2 - Bagnall, Anthony

A2 - Guyet, Thomas

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A2 - Ifrim, Georgiana

PB - Springer

Y2 - 14 September 2020 through 18 October 2020

ER -

Dhont M, Tsiporkova E, Boeva V. Layered Integration Approach for Multi-view Analysis of Temporal Data. In Lemaire V, Malinowski S, Bagnall A, Guyet T, Tavenard R, Ifrim G, redacteurs, 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. Springer. 2020. blz. 138-154. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-65742-0_10

Layered Integration Approach for Multi-view Analysis of Temporal Data

Samenvatting

Publicatie series

Conference

Bibliografische nota

Toegang tot document

Andere bestanden en links

Vingerafdruk

Projecten

BRGDOC9: Applied PhD 2019: MISTic: Trendanalyse met meerdere bronnen voor intelligente transportatie systemen

Onderzoekersoutput

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Activiteiten