# Data Integrity Error Localization in Networked Systems with Missing Data

arXiv: https://arxiv.org/abs/2207.02102 · HTML: https://mlsanity.com/paper/2207.02102/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [The Abstention Protocol: RCA for Clos Fabrics](https://mlsanity.com/paper/2608.21412/) (2026)

## Abstract (arXiv)

Most recent network failure diagnosis systems focused on data center networks where complex measurement systems can be deployed to derive routing information and ensure network coverage in order to achieve accurate and fast fault localization. In this paper, we target wide-area networks that support data-intensive distributed applications. We first present a new multi-output prediction model that directly maps the application level observations to localize the system component failures. In reality, this application-centric approach may face the missing data challenge as some input (feature) data to the inference models may be missing due to incomplete or lost measurements in wide area networks. We show that the presented prediction model naturally allows the it multivariate imputation to recover the missing data. We evaluate multiple imputation algorithms and show that the prediction performance can be improved significantly in a large-scale network. As far as we know, this is the first study on the missing data issue and applying imputation techniques in network failure localization.