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

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. 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.

Material Protection for Citarum River-Bed by Local Scour used What-If Analysis

“…Although goals are often defined as optimization problems, their actual solutions are usually not a user's primary concern. Instead, the user seeks to gain insight beyond knowing the optimal solution to take the right decision [5].…”

“…Types of what-if analysis vary in how complex systems are modeled, including hypothetical modifications [6], stochastic simulations [7], or statistical methods [8]. [5] study what-if analysis for data exploration, where the model of a system is non-trivial. Although the setting is similar to the sensitivity analysis [9], the traditional technique considers only one dimensional and, thus, one objective outcome.…”

“…Although the setting is similar to the sensitivity analysis [9], the traditional technique considers only one dimensional and, thus, one objective outcome. The [5] use case is a scenario with many conflicting purposes.…”

FactCatch: Incremental Pay-as-You-Go Fact Checking with Minimal User Effort

“…Extracting factual knowledge from Web data plays an important role in the construction of knowledge bases (KB) such as Freebase, YAGO and DBpedia, which store millions of facts about various domains [8].Some systems focus on extracting candidate facts (aka claims) [4], but leave the credibility assessment for expert services [1]. Other systems focus on static classification of claims, but neglect the dynamic and evolving nature of the Web [15,16].…”

Minimizing Efforts in Reconciling Participatory Sensing Data

“…Participatory sensing. In recent years, there has been an increased interest on participatory sensing due to the increasing number of personal sensors such as smartphones, GPS devices, and cameras [12,30,32,51,59,60]. Participatory sensing campaigns often recruit participants to provide measurements on a particular region or a population.…”