End-user development for interactive data analytics: Uncertainty, correlation and user confidence

Zhou, J; Arshad, SZ; Wang, X; Li, Z; Feng, D; Chen, F

End-user development for interactive data analytics: Uncertainty, correlation and user confidence

Zhou, J

Arshad, SZ Wang, X Li, Z

Feng, D Chen, F

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Affective Computing, 2018, 9, (3), pp. 383-395
Issue Date:: 2018-01-01

Closed Access

	Filename	Description	Size
	End-User_Development_for_Interactive_Data_Analytics_Uncertainty_Correlation_and_User_Confidence.pdf	Published version	817.07 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhou, J https://orcid.org/0000-0001-6034-644X
dc.contributor.author	Arshad, SZ
dc.contributor.author	Wang, X
dc.contributor.author	Li, Z https://orcid.org/0000-0002-0784-157X
dc.contributor.author	Feng, D
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2022-08-29T05:46:47Z
dc.date.available	2022-08-29T05:46:47Z
dc.date.issued	2018-01-01
dc.identifier.citation	IEEE Transactions on Affective Computing, 2018, 9, (3), pp. 383-395
dc.identifier.issn	1949-3045
dc.identifier.issn	1949-3045
dc.identifier.uri	http://hdl.handle.net/10453/161031
dc.description.abstract	This paper investigates End-User Development (EUD) for interactive data-analytic interfaces—building upon the ideas of making machine learning transparent. The research is carried out in a business operation environment (water pipe failure prediction in our case) motivated to integrate advanced analytics into decision-making processes of an urban Internet of Things (IoT) concept. We explore effects of revealing uncertainty and correlation on user confidence in a data-driven decision making scenario. It was found that user confidence varied significantly amongst various user groups when different machine learning models were displayed with/without supplementary information. Galvanic Skin Response (GSR) signals were analyzed and shown as reasonable indices for predicting user confidence levels. Supplementary data visualizations (of inherent uncertainty and correlation in data) contributed to explicability principles while GSR indexing added towards correctibility principles. We recommend transparent machine learning as the key to effective EUD for interactive data analytics.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Affective Computing
dc.relation.isbasedon	10.1109/TAFFC.2017.2723402
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 1702 Cognitive Sciences
dc.title	End-user development for interactive data analytics: Uncertainty, correlation and user confidence
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
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/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-29T05:46:46Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	3

Abstract:

This paper investigates End-User Development (EUD) for interactive data-analytic interfaces—building upon the ideas of making machine learning transparent. The research is carried out in a business operation environment (water pipe failure prediction in our case) motivated to integrate advanced analytics into decision-making processes of an urban Internet of Things (IoT) concept. We explore effects of revealing uncertainty and correlation on user confidence in a data-driven decision making scenario. It was found that user confidence varied significantly amongst various user groups when different machine learning models were displayed with/without supplementary information. Galvanic Skin Response (GSR) signals were analyzed and shown as reasonable indices for predicting user confidence levels. Supplementary data visualizations (of inherent uncertainty and correlation in data) contributed to explicability principles while GSR indexing added towards correctibility principles. We recommend transparent machine learning as the key to effective EUD for interactive data analytics.

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

http://hdl.handle.net/10453/161031