Tensor Decompositions in Multimodal Big Data: Studying Multiway Behavioral Patterns

Chaczko, Z; Rakhi Ajayan, A; Al-Doghman, F

Tensor Decompositions in Multimodal Big Data: Studying Multiway Behavioral Patterns

Chaczko, Z

Rakhi Ajayan, A Al-Doghman, F

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Smart Innovations in Engineering and Technology, 2020, 1, 15 pp. 1 - 296
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.contributor.author	Rakhi Ajayan, A	en_US
dc.contributor.author	Al-Doghman, F https://orcid.org/0000-0002-1020-8097	en_US
dc.contributor.editor	Klempous, R	en_US
dc.contributor.editor	Nikodem, J	en_US
dc.date.issued	2020	en_US
dc.identifier.citation	Smart Innovations in Engineering and Technology, 2020, 1, 15 pp. 1 - 296	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137319
dc.description.abstract	Preset day cyber-physical systems (CPS) are the confluence of very large data sets, tight time constraints, and heterogeneous hardware units, ridden with latency and volume constraints, demanding newer analytic perspectives. Their system logistics can be well-defined by the data-streams’ behavioural trends across various modalities, without numerical restrictions, favouring resource-saving over methods of investigating individual component features and operations. The aim of this paper is to demonstrate how behaviour patterns and related anomalies comprehensively define a CPS. Tensor decompositions are hypothesized as the solution in the context of multimodal smart-grid-originated Big Data analysis. Tensorial data representation is demonstrated to capture the complex knowledge encompassed in these data flows. The uniqueness of this approach is highlighted in the modified multiway anomaly patterns models. In addition, higher-order data preparation schemes, design and implementation of tensorial frameworks and experimental-analysis are final outcomes.	en_US
dc.publisher	Springer Nature	en_US
dc.relation.ispartof	Smart Innovations in Engineering and Technology	en_US
dc.relation.isbasedon	10.1007/978-3-030-32861-0_7	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Tensor Decompositions in Multimodal Big Data: Studying Multiway Behavioral Patterns	en_US
dc.type	Chapter
utslib.citation.volume	15	en_US
utslib.location	Cham, Switzerland	en_US
utslib.citation.edition	1	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true	en_US
pubs.edition	1	en_US
pubs.place-of-publication	Cham, Switzerland	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

Preset day cyber-physical systems (CPS) are the confluence of very large data sets, tight time constraints, and heterogeneous hardware units, ridden with latency and volume constraints, demanding newer analytic perspectives. Their system logistics can be well-defined by the data-streams’ behavioural trends across various modalities, without numerical restrictions, favouring resource-saving over methods of investigating individual component features and operations. The aim of this paper is to demonstrate how behaviour patterns and related anomalies comprehensively define a CPS. Tensor decompositions are hypothesized as the solution in the context of multimodal smart-grid-originated Big Data analysis. Tensorial data representation is demonstrated to capture the complex knowledge encompassed in these data flows. The uniqueness of this approach is highlighted in the modified multiway anomaly patterns models. In addition, higher-order data preparation schemes, design and implementation of tensorial frameworks and experimental-analysis are final outcomes.

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http://hdl.handle.net/10453/137319