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dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorZheng, Kai
dc.contributor.authorWeidlich, Matthias
dc.contributor.authorZheng, Bolong
dc.contributor.authorYin, Hongzhi
dc.contributor.authorNguyen, Thanh Tam
dc.contributor.authorStantic, Bela
dc.date.accessioned2019-05-29T13:09:38Z
dc.date.available2019-05-29T13:09:38Z
dc.date.issued2018
dc.identifier.isbn9781538655207
dc.identifier.issn1084-4627
dc.identifier.doi10.1109/ICDE.2018.00018
dc.identifier.urihttp://hdl.handle.net/10072/382451
dc.description.abstractWhat-if analysis is a data-intensive exploration to inspect how changes in a set of input parameters of a model influence some outcomes. It is motivated by a user trying to understand the sensitivity of a model to a certain parameter in order to reach a set of goals that are defined over the outcomes. To avoid an exploration of all possible combinations of parameter values, efficient what-if analysis calls for a partitioning of parameter values into data ranges and a unified representation of the obtained outcomes per range. Traditional techniques to capture data ranges, such as histograms, are limited to one outcome dimension. Yet, in practice, what-if analysis often involves conflicting goals that are defined over different dimensions of the outcome. Working on each of those goals independently cannot capture the inherent trade-off between them. In this paper, we propose techniques to recommend data ranges for what-if analysis, which capture not only data regularities, but also the trade-off between conflicting goals. Specifically, we formulate a parametric data partitioning problem and propose a method to find an optimal solution for it. Targeting scalability to large datasets, we further provide a heuristic solution to this problem. By theoretical and empirical analyses, we establish performance guarantees in terms of runtime and result quality.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE Computer Society
dc.publisher.placeUnited States
dc.relation.ispartofchapter43307
dc.relation.ispartofconferencename34th IEEE International Conference on Data Engineering Workshops (ICDEW)
dc.relation.ispartofconferencetitle2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE)
dc.relation.ispartofdatefrom2018-04-16
dc.relation.ispartofdateto2018-04-19
dc.relation.ispartoflocationParis, FRANCE
dc.relation.ispartofpagefrom89
dc.relation.ispartofpagefrom12 pages
dc.relation.ispartofpageto100
dc.relation.ispartofpageto12 pages
dc.subject.fieldofresearchDatabase systems
dc.subject.fieldofresearchcode460505
dc.titleWhat-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2018 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.
gro.hasfulltextFull Text
gro.griffith.authorStantic, Bela
gro.griffith.authorNguyen, Henry
gro.griffith.authorNguyen, Thanh Tam


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