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  • What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration

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    Accepted Manuscript (AM)
    Author(s)
    Nguyen, Quoc Viet Hung
    Zheng, Kai
    Weidlich, Matthias
    Zheng, Bolong
    Yin, Hongzhi
    Nguyen, Thanh Tam
    Stantic, Bela
    Griffith University Author(s)
    Stantic, Bela
    Nguyen, Henry
    Nguyen, Thanh Tam
    Year published
    2018
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    Abstract
    What-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. ...
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    What-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.
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    Conference Title
    2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE)
    DOI
    https://doi.org/10.1109/ICDE.2018.00018
    Copyright Statement
    © 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.
    Subject
    Database systems
    Publication URI
    http://hdl.handle.net/10072/382451
    Collection
    • Conference outputs

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