Big Data Analytics Has Little to Do with Analytics

Rabhi, F; Bandara, M; Namvar, A; Demirors, O

Big Data Analytics Has Little to Do with Analytics

Rabhi, F Bandara, M Namvar, A Demirors, O

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Service Research and Innovation, 2018, 234, pp. 3-17
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Rabhi, F
dc.contributor.author	Bandara, M
dc.contributor.author	Namvar, A
dc.contributor.author	Demirors, O
dc.date	2017-10-19
dc.date.accessioned	2022-12-25T20:28:11Z
dc.date.available	2022-12-25T20:28:11Z
dc.date.issued	2018-01-01
dc.identifier.citation	Service Research and Innovation, 2018, 234, pp. 3-17
dc.identifier.isbn	9783319765860
dc.identifier.issn	1865-1348
dc.identifier.issn	1865-1356
dc.identifier.uri	http://hdl.handle.net/10453/164560
dc.description.abstract	As big data analytics is adapted across multitude of domains and applications there is a need for new platforms and architectures that support analytic solution engineering as a lean and iterative process. In this paper we discuss how different software development processes can be adapted to data analytic process engineering, incorporating service oriented architecture, scientific workflows, model driven engineering and semantic technology. Based on the experience obtained through ADAGE framework [1] and the findings of the survey on how semantic modeling is used for data analytic solution engineering [6], we propose two research directions - big data analytic development lifecycle and data analytic knowledge management for lean and flexible data analytic platforms.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Service Research and Innovation
dc.relation.ispartof	Australian Symposium on Service Research and Innovation
dc.relation.ispartofseries	Lecture Notes in Business Information Processing
dc.relation.isbasedon	10.1007/978-3-319-76587-7_1
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Big Data Analytics Has Little to Do with Analytics
dc.type	Conference Proceeding
utslib.citation.volume	234
utslib.location.activity	Sydney, NSW, Australia
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/Strength - AAII - Australian Artificial Intelligence Institute
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	2022-12-25T20:28:09Z
pubs.finish-date	2017-10-20
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2017-10-19
pubs.volume	234
dc.location	Switzerland

Abstract:

As big data analytics is adapted across multitude of domains and applications there is a need for new platforms and architectures that support analytic solution engineering as a lean and iterative process. In this paper we discuss how different software development processes can be adapted to data analytic process engineering, incorporating service oriented architecture, scientific workflows, model driven engineering and semantic technology. Based on the experience obtained through ADAGE framework [1] and the findings of the survey on how semantic modeling is used for data analytic solution engineering [6], we propose two research directions - big data analytic development lifecycle and data analytic knowledge management for lean and flexible data analytic platforms.

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http://hdl.handle.net/10453/164560