Utility-Driven Data Analytics on Uncertain Data

Gan, W; Lin, JCW; Chao, HC; Vasilakos, AV; Yu, PS

Utility-Driven Data Analytics on Uncertain Data

Gan, W Lin, JCW Chao, HC Vasilakos, AV Yu, PS

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Systems Journal, 2020, 14, (3), pp. 4442-4453
Issue Date:: 2020-09-01

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Field	Value	Language
dc.contributor.author	Gan, W
dc.contributor.author	Lin, JCW
dc.contributor.author	Chao, HC
dc.contributor.author	Vasilakos, AV
dc.contributor.author	Yu, PS
dc.date.accessioned	2021-02-02T05:45:59Z
dc.date.available	2021-02-02T05:45:59Z
dc.date.issued	2020-09-01
dc.identifier.citation	IEEE Systems Journal, 2020, 14, (3), pp. 4442-4453
dc.identifier.issn	1932-8184
dc.identifier.issn	1937-9234
dc.identifier.uri	http://hdl.handle.net/10453/145736
dc.description.abstract	© 2007-2012 IEEE. Modern Internet of Things (IoT) applications generate massive amounts of data, much of them in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different levels of importance (e.g., unit risk/utility of items, interestingness, cost, or weight). Many existing approaches in data mining and analytics have limitations, such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented in this article. As a general utility-driven uncertain data mining model, HUPNU can extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for intrusion detection, risk prediction, manufacturing management, and decision-making, among others. By using the developed vertical Probability-Utility list with the positive and negative utilities structure, as well as several effective pruning strategies, experiments showed that the developed HUPNU approach with the pruning strategies performed great in mining the qualified patterns efficiently and effectively.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Systems Journal
dc.relation.isbasedon	10.1109/JSYST.2020.2979279
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Operations Research
dc.title	Utility-Driven Data Analytics on Uncertain Data
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0906 Electrical and Electronic Engineering
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-02-02T05:45:50Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	3

Abstract:

© 2007-2012 IEEE. Modern Internet of Things (IoT) applications generate massive amounts of data, much of them in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different levels of importance (e.g., unit risk/utility of items, interestingness, cost, or weight). Many existing approaches in data mining and analytics have limitations, such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented in this article. As a general utility-driven uncertain data mining model, HUPNU can extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for intrusion detection, risk prediction, manufacturing management, and decision-making, among others. By using the developed vertical Probability-Utility list with the positive and negative utilities structure, as well as several effective pruning strategies, experiments showed that the developed HUPNU approach with the pruning strategies performed great in mining the qualified patterns efficiently and effectively.

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http://hdl.handle.net/10453/145736