Targets of Unequal Importance Using the Concept of Stratification in a Big Data Environment

Asadabadi, MR; Saberi, M; Chang, E

Targets of Unequal Importance Using the Concept of Stratification in a Big Data Environment

Asadabadi, MR Saberi, M

Chang, E

Permalink

Publication Type:: Journal Article
Citation:: International Journal of Fuzzy Systems, 2018, 20 (4), pp. 1373 - 1384
Issue Date:: 2018-04-01

Closed Access

	Filename	Description	Size
	Asadabadi2018_Article_TargetsOfUnequalImportanceUsin.pdf	Published Version	1.41 MB	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	Asadabadi, MR	en_US
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078	en_US
dc.contributor.author	Chang, E	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	International Journal of Fuzzy Systems, 2018, 20 (4), pp. 1373 - 1384	en_US
dc.identifier.issn	1562-2479	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133134
dc.description.abstract	© 2017, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. The concept of stratification (CST) has recently been proposed as an innovative approach in problem solving. CST takes a recursive approach to solve problems. It considers a system which has to transition through states until it arrives to a state which belongs to a desired set of states, namely a target set. The states can be stratified by enlarging the target (absorbing adjacent states). Incremental enlargement is a means to identify possible paths to achieve the target. Such an enlargement can also be used to degrade the target when the original target is not reachable. Although the characteristics of the concept, such as incremental enlargement, enhance its potential application in robotics, artificial intelligence, and planning and monitoring, there is a major shortcoming in the approach, namely its inability to consider targets of unequal importance. This study considers two targets of unequal importance for the system in CST, labelled Bi-Objective CST model (BOCST). In comparison with the original proposed CST model in this research, a version of CST with finite states which is much easier to apply than the original CST is proposed, labelled fuzzy CST. Following that, a combination of Fuzzy CST and BOCST (FBO-CST) is proposed. The model is then employed to address a restaurant selection problem using data from Google. The example illustrates how the model should be applied in a big data environment. By defining finite state CST and considering targets of unequal importance, this study is expected to facilitate future applications of CST.	en_US
dc.relation.ispartof	International Journal of Fuzzy Systems	en_US
dc.relation.isbasedon	10.1007/s40815-017-0430-y	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Targets of Unequal Importance Using the Concept of Stratification in a Big Data Environment	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	20	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

© 2017, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. The concept of stratification (CST) has recently been proposed as an innovative approach in problem solving. CST takes a recursive approach to solve problems. It considers a system which has to transition through states until it arrives to a state which belongs to a desired set of states, namely a target set. The states can be stratified by enlarging the target (absorbing adjacent states). Incremental enlargement is a means to identify possible paths to achieve the target. Such an enlargement can also be used to degrade the target when the original target is not reachable. Although the characteristics of the concept, such as incremental enlargement, enhance its potential application in robotics, artificial intelligence, and planning and monitoring, there is a major shortcoming in the approach, namely its inability to consider targets of unequal importance. This study considers two targets of unequal importance for the system in CST, labelled Bi-Objective CST model (BOCST). In comparison with the original proposed CST model in this research, a version of CST with finite states which is much easier to apply than the original CST is proposed, labelled fuzzy CST. Following that, a combination of Fuzzy CST and BOCST (FBO-CST) is proposed. The model is then employed to address a restaurant selection problem using data from Google. The example illustrates how the model should be applied in a big data environment. By defining finite state CST and considering targets of unequal importance, this study is expected to facilitate future applications of CST.

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

http://hdl.handle.net/10453/133134