A Computational Framework for Railway Incident Analysis: from Data Mining to Data Visualization
Sergio Viademonte, Cleidson R. B. de Souza, Nikolas Carneiro, Jair Camargo Ferreira, Wallace P. Lira · Journal of the Association for Information Systems · 2018
This document presents a computational framework for railways incident analysis and risk assessment. The computational framework combines data mining technologies with a visual programming framework, to generate knowledge from incident data and allows users to explore such knowledge, to obtain insights about conditions that impact railways safety. This document describes the workflow on the development of this computational framework, discusses data processing activities, the evaluation of the developed computational framework, and the visual knowledge representation capability.