# Consistency Analysis for Massively Inconsistent Datasets in Bound-to-Bound Data Collaboration

## TL;DR

This paper introduces a new vector consistency measure within the B2BDC framework to identify sources of inconsistency in complex datasets, demonstrated through gas combustion case studies.

## Contribution

It presents a novel constraint relaxation-based approach for analyzing and diagnosing inconsistencies in large datasets with multiple conflicting observations.

## Key findings

- The vector consistency measure effectively identifies problematic models and observations.
- Application to gas combustion datasets shows improved inconsistency detection.
- The method outperforms previous approaches in real-world scenarios.

## Abstract

Bound-to-Bound Data Collaboration (B2BDC) provides a natural framework for addressing both forward and inverse uncertainty quantification problems. In this approach, QOI (quantity of interest) models are constrained by related experimental observations with interval uncertainty. A collection of such models and observations is termed a dataset and carves out a feasible region in the parameter space. If a dataset has a nonempty feasible set, it is said to be consistent. In real-world applications, it is often the case that collections of experiments and observations are inconsistent. Revealing the source of this inconsistency, i.e., identifying which models and/or observations are problematic, is essential before a dataset can be used for prediction. To address this issue, we introduce a constraint relaxation-based approach, entitled the vector consistency measure, for investigating datasets with numerous sources of inconsistency. The benefits of this vector consistency measure over a previous method of consistency analysis are demonstrated in two realistic gas combustion examples.