Visualizing multimodal big data anomaly patterns in higher-order feature spaces

Ajayan, AR; Al-Doghman, F; Chaczko, Z

Visualizing multimodal big data anomaly patterns in higher-order feature spaces

Ajayan, AR

Al-Doghman, F

Chaczko, Z

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Publication Type:: Conference Proceeding
Citation:: 26th International Conference on Systems Engineering, ICSEng 2018 - Proceedings, 2019
Issue Date:: 2019-02-08

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Field	Value	Language
dc.contributor.author	Ajayan, AR https://orcid.org/0000-0002-9147-1048	en_US
dc.contributor.author	Al-Doghman, F https://orcid.org/0000-0002-1020-8097	en_US
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.date.issued	2019-02-08	en_US
dc.identifier.citation	26th International Conference on Systems Engineering, ICSEng 2018 - Proceedings, 2019	en_US
dc.identifier.isbn	9781538678343	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130537
dc.description.abstract	© 2018 IEEE. The world today, as we know it, is profuse with information about humans and objects. Datasets generated by cyber-physical systems are orders of magnitude larger than their current information processing capabilities. Tapping into these big data flows to uncover much deeper perceptions into the functioning, operational logic and smartness levels attainable has been investigated for quite a while. Knowledge Discovery Representation capabilities across mutiple modalities holds much scope in this direction, with regards to their information holding potential. This paper investigates the applicability of an arithmetic tool Tensor Decompositions and Factorizations in this scenario. Higher order datasets are decomposed for Anomaly Pattern capture which encases intelligence along multiple modes of data flow. Preliminary investigations based on data derived from Smart Grid Smart City Project are compliant with our hypothesis. The results proved that Abnormal patterns detected in decomposed Tensor factors encompass deep information energy content from Big Data as efficiently as other Pattern Extraction and Knowledge Discovery frameworks, while salvaging time and resources.	en_US
dc.relation.ispartof	26th International Conference on Systems Engineering, ICSEng 2018 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICSENG.2018.8638195	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Visualizing multimodal big data anomaly patterns in higher-order feature spaces	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	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.publication-status	Published	en_US

Abstract:

© 2018 IEEE. The world today, as we know it, is profuse with information about humans and objects. Datasets generated by cyber-physical systems are orders of magnitude larger than their current information processing capabilities. Tapping into these big data flows to uncover much deeper perceptions into the functioning, operational logic and smartness levels attainable has been investigated for quite a while. Knowledge Discovery Representation capabilities across mutiple modalities holds much scope in this direction, with regards to their information holding potential. This paper investigates the applicability of an arithmetic tool Tensor Decompositions and Factorizations in this scenario. Higher order datasets are decomposed for Anomaly Pattern capture which encases intelligence along multiple modes of data flow. Preliminary investigations based on data derived from Smart Grid Smart City Project are compliant with our hypothesis. The results proved that Abnormal patterns detected in decomposed Tensor factors encompass deep information energy content from Big Data as efficiently as other Pattern Extraction and Knowledge Discovery frameworks, while salvaging time and resources.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/130537