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

Quoc Viet Hung Nguyen, Kai Zheng, Matthias Weidlich, Bolong Zheng, Hongzhi Yin, Thanh Tam Nguyen, Bela Stantic

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1 Citationer (Scopus)

Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Antal sider12
ForlagIEEE
Publikationsdato24 okt. 2018
Sider89-100
Artikelnummer8509239
ISBN (Trykt)978-1-5386-5521-4
ISBN (Elektronisk)978-1-5386-5520-7
DOI
StatusUdgivet - 24 okt. 2018
Begivenhed34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, Frankrig
Varighed: 16 apr. 201819 apr. 2018

Konference

Konference34th IEEE International Conference on Data Engineering, ICDE 2018
LandFrankrig
ByParis
Periode16/04/201819/04/2018

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