Inter-space Machine Learning in Smart Environments

Amin Anjomshoaa; Edward Curry

doi:10.1007/978-3-030-57321-8_30

Inter-space Machine Learning in Smart Environments

Amin Anjomshoaa
, Edward Curry

Lero – The Irish Software Research Centre

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

5 Citations (Scopus)

Abstract

Today, our built environment is not only producing large amounts of data, but –driven by the Internet of Things (IoT) paradigm– it is also starting to talk back and communicate with its inhabitants and the surrounding systems and processes. In order to unleash the power of IoT enabled environments, they need to be trained and configured for space-specific properties and semantics. This paper investigates the potential of communication and transfer learning between smart environments for a seamless and automatic transfer of personalized services and machine learning models. To this end, we explore different knowledge types in context of smart built environments and propose a collaborative framework based on Knowledge Graph principles and IoT paradigm for supporting transfer learning between spaces.

Original language	English
Title of host publication	Machine Learning and Knowledge Extraction - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Proceedings
Editors	Andreas Holzinger, Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl, Edgar Weippl
Publisher	Springer
Pages	535-549
Number of pages	15
ISBN (Print)	9783030573201
DOIs	https://doi.org/10.1007/978-3-030-57321-8_30
Publication status	Published - 2020
Externally published	Yes
Event	4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2020 - Dublin, Ireland Duration: 25 Aug 2020 → 28 Aug 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12279 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2020
Country/Territory	Ireland
City	Dublin
Period	25/08/20 → 28/08/20

Keywords

Knowledge graph
Smart environment
Transfer learning

Access to Document

10.1007/978-3-030-57321-8_30

Cite this

Anjomshoaa, A., & Curry, E. (2020). Inter-space Machine Learning in Smart Environments. In A. Holzinger, A. Holzinger, P. Kieseberg, A. M. Tjoa, E. Weippl, & E. Weippl (Eds.), Machine Learning and Knowledge Extraction - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Proceedings (pp. 535-549). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12279 LNCS). Springer . https://doi.org/10.1007/978-3-030-57321-8_30

Anjomshoaa, Amin ; Curry, Edward. / Inter-space Machine Learning in Smart Environments. Machine Learning and Knowledge Extraction - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Proceedings. editor / Andreas Holzinger ; Andreas Holzinger ; Peter Kieseberg ; A Min Tjoa ; Edgar Weippl ; Edgar Weippl. Springer , 2020. pp. 535-549 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Today, our built environment is not only producing large amounts of data, but –driven by the Internet of Things (IoT) paradigm– it is also starting to talk back and communicate with its inhabitants and the surrounding systems and processes. In order to unleash the power of IoT enabled environments, they need to be trained and configured for space-specific properties and semantics. This paper investigates the potential of communication and transfer learning between smart environments for a seamless and automatic transfer of personalized services and machine learning models. To this end, we explore different knowledge types in context of smart built environments and propose a collaborative framework based on Knowledge Graph principles and IoT paradigm for supporting transfer learning between spaces.",

keywords = "Knowledge graph, Smart environment, Transfer learning",

author = "Amin Anjomshoaa and Edward Curry",

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doi = "10.1007/978-3-030-57321-8\_30",

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isbn = "9783030573201",

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Anjomshoaa, A & Curry, E 2020, Inter-space Machine Learning in Smart Environments. in A Holzinger, A Holzinger, P Kieseberg, AM Tjoa, E Weippl & E Weippl (eds), Machine Learning and Knowledge Extraction - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12279 LNCS, Springer , pp. 535-549, 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2020, Dublin, Ireland, 25/08/20. https://doi.org/10.1007/978-3-030-57321-8_30

Inter-space Machine Learning in Smart Environments. / Anjomshoaa, Amin; Curry, Edward.
Machine Learning and Knowledge Extraction - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Proceedings. ed. / Andreas Holzinger; Andreas Holzinger; Peter Kieseberg; A Min Tjoa; Edgar Weippl; Edgar Weippl. Springer , 2020. p. 535-549 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12279 LNCS).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - Inter-space Machine Learning in Smart Environments

AU - Anjomshoaa, Amin

AU - Curry, Edward

PY - 2020

Y1 - 2020

N2 - Today, our built environment is not only producing large amounts of data, but –driven by the Internet of Things (IoT) paradigm– it is also starting to talk back and communicate with its inhabitants and the surrounding systems and processes. In order to unleash the power of IoT enabled environments, they need to be trained and configured for space-specific properties and semantics. This paper investigates the potential of communication and transfer learning between smart environments for a seamless and automatic transfer of personalized services and machine learning models. To this end, we explore different knowledge types in context of smart built environments and propose a collaborative framework based on Knowledge Graph principles and IoT paradigm for supporting transfer learning between spaces.

AB - Today, our built environment is not only producing large amounts of data, but –driven by the Internet of Things (IoT) paradigm– it is also starting to talk back and communicate with its inhabitants and the surrounding systems and processes. In order to unleash the power of IoT enabled environments, they need to be trained and configured for space-specific properties and semantics. This paper investigates the potential of communication and transfer learning between smart environments for a seamless and automatic transfer of personalized services and machine learning models. To this end, we explore different knowledge types in context of smart built environments and propose a collaborative framework based on Knowledge Graph principles and IoT paradigm for supporting transfer learning between spaces.

KW - Knowledge graph

KW - Smart environment

KW - Transfer learning

UR - https://www.scopus.com/pages/publications/85090177996

U2 - 10.1007/978-3-030-57321-8_30

DO - 10.1007/978-3-030-57321-8_30

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

SP - 535

EP - 549

BT - Machine Learning and Knowledge Extraction - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Proceedings

A2 - Holzinger, Andreas

A2 - Kieseberg, Peter

A2 - Tjoa, A Min

A2 - Weippl, Edgar

PB - Springer

T2 - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2020

Y2 - 25 August 2020 through 28 August 2020

ER -

Anjomshoaa A, Curry E. Inter-space Machine Learning in Smart Environments. In Holzinger A, Holzinger A, Kieseberg P, Tjoa AM, Weippl E, Weippl E, editors, Machine Learning and Knowledge Extraction - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Proceedings. Springer . 2020. p. 535-549. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-57321-8_30

Inter-space Machine Learning in Smart Environments

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Keywords

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