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.
|Titel||Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018|
|Publikationsdato||24 okt. 2018|
|Status||Udgivet - 24 okt. 2018|
|Begivenhed||34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, Frankrig|
Varighed: 16 apr. 2018 → 19 apr. 2018
|Konference||34th IEEE International Conference on Data Engineering, ICDE 2018|
|Periode||16/04/2018 → 19/04/2018|