Effective Order Preserving Estimation Method

Chen, C; Wang, W; Wang, X; Yang, S

Effective Order Preserving Estimation Method

Chen, C Wang, W Wang, X Yang, S

Permalink

Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Databases Theory and Applications: Lecture Notes in Computer Science, 2016, 9877 pp. 369 - 380
Issue Date:: 2016

Closed Access

	Filename	Description	Size
	PAC-10008637.pdf	Published version	610.36 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	Chen, C	en_US
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Yang, S	en_US
dc.date	2016-09-28	en_US
dc.date.issued	2016	en_US
dc.identifier.citation	Databases Theory and Applications: Lecture Notes in Computer Science, 2016, 9877 pp. 369 - 380	en_US
dc.identifier.isbn	978-3-319-46921-8	en_US
dc.identifier.uri	http://hdl.handle.net/10453/102943
dc.description.abstract	Order preserving estimation is an estimation method that can retain the original order of the population parameters of interest. It is an important tool in many applications such as data visualization. In this paper, we focus on the population mean as our primary estimation function, and propose effective query processing strategy that can preserve the estimated order to be correct with probabilistic guarantees. We define the cost function as the number of samples taken for all the groups, and our goal is to make the sample size as small as possible. We compare our methods with state-of-the-art near-optimal algorithm in the literature, and achieve up to 80%80% reduction in the total sample size.	en_US
dc.publisher	Springer	en_US
dc.relation.ispartof	Databases Theory and Applications: Lecture Notes in Computer Science	en_US
dc.relation.ispartof	Australasian Database Conference	en_US
dc.relation.isbasedon	10.1007/978-3-319-46922-5_29	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Effective Order Preserving Estimation Method	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9877	en_US
utslib.location.activity	Sydney, NSW	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
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2016-09-29	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2016-09-28	en_US
pubs.volume	9877	en_US

Abstract:

Order preserving estimation is an estimation method that can retain the original order of the population parameters of interest. It is an important tool in many applications such as data visualization. In this paper, we focus on the population mean as our primary estimation function, and propose effective query processing strategy that can preserve the estimated order to be correct with probabilistic guarantees. We define the cost function as the number of samples taken for all the groups, and our goal is to make the sample size as small as possible. We compare our methods with state-of-the-art near-optimal algorithm in the literature, and achieve up to 80%80% reduction in the total sample size.

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

http://hdl.handle.net/10453/102943