# A Conceptual Framework for Supporting a Rapid Design of Web Applications for Data Analysis of Electrical Quality Assurance Data for the LHC

## TL;DR

This paper proposes a conceptual framework to facilitate rapid development of web applications for analyzing large electrical quality assurance datasets in the LHC, integrating machine learning and system components.

## Contribution

It introduces a novel framework that streamlines the design of web tools for ELQA data analysis in complex scientific infrastructure.

## Key findings

- Framework supports quick prototyping of ELQA web applications
- Integration with machine learning enhances data analysis capabilities
- Use case demonstrates practical application for LHC electrical QA

## Abstract

The Large Hadron Collider (LHC) is one of the most complex machines ever build. It is composed of many components which constitute a large system. The tunnel and the accelerator is just one of a very critical fraction of the whole LHC infrastructure. Hardware comissioning as one of the critical processes before running the LHC is implemented during the Long Shutdown (LS) states of the macine, where Electrical Quality Assurance (ELQA) is one of its key components. Here a huge data is collected when implementing various ELQA electrical tests. In this paper we present a conceptual framework for supporting a rapid design of web applications for ELQA data analysis. We show a framework's main components, their possible integration with other systems and machine learning algorithms and a simple use case of prototyping an application for Electrical Quality Assurance of the LHC.