A Data-Analytics Approach for Enterprise Resilience

Xu, D; Tsang, IW; Chew, EK; Siclari, C; Kaul, V

A Data-Analytics Approach for Enterprise Resilience

Xu, D Tsang, IW

Chew, EK

Siclari, C Kaul, V

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Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2019, 34 (3), pp. 6 - 18
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Xu, D	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Chew, EK https://orcid.org/0000-0002-5850-2455	en_US
dc.contributor.author	Siclari, C	en_US
dc.contributor.author	Kaul, V	en_US
dc.date.available	2021-05-25T19:02:06Z
dc.date.issued	2019-05-01	en_US
dc.identifier.citation	IEEE Intelligent Systems, 2019, 34 (3), pp. 6 - 18	en_US
dc.identifier.issn	1541-1672	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135192
dc.description.abstract	© 2001-2011 IEEE. Enterprise resilience plays an important role to prevent business services from disruptions caused by human-induced disasters such as failed change implementations and software bugs. Traditional expert-centric approach has difficulty to maintain continued critical business functions because the disasters can often only be handled after their occurrence. This paper introduces a data-analytics approach, which leverages system monitoring data for the enterprise resilience. With the power of data mining and machine learning techniques, we build an intelligent business analytics system to detect the potential disruptions proactively, and to assist the operational team for enterprise resilience enhancement. We demonstrate the effectiveness of our approach on a real enterprise system monitoring dataset in simulation.	en_US
dc.relation.ispartof	IEEE Intelligent Systems	en_US
dc.relation.isbasedon	10.1109/MIS.2019.2918092	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A Data-Analytics Approach for Enterprise Resilience	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	34	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	34	en_US

Abstract:

© 2001-2011 IEEE. Enterprise resilience plays an important role to prevent business services from disruptions caused by human-induced disasters such as failed change implementations and software bugs. Traditional expert-centric approach has difficulty to maintain continued critical business functions because the disasters can often only be handled after their occurrence. This paper introduces a data-analytics approach, which leverages system monitoring data for the enterprise resilience. With the power of data mining and machine learning techniques, we build an intelligent business analytics system to detect the potential disruptions proactively, and to assist the operational team for enterprise resilience enhancement. We demonstrate the effectiveness of our approach on a real enterprise system monitoring dataset in simulation.

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http://hdl.handle.net/10453/135192