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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Number of pages12
Publication date24 Oct 2018
Article number8509239
ISBN (Print)978-1-5386-5521-4
ISBN (Electronic)978-1-5386-5520-7
Publication statusPublished - 24 Oct 2018
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018


Conference34th IEEE International Conference on Data Engineering, ICDE 2018


  • Data partitioning
  • Pareto analysis
  • what if analysis


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