A process mining approach in big data analysis and modeling decision making risks for measuring environmental health in institutions

Abstract

This paper aimed to introduce a process-mining framework for measuring the status of environmental health in institutions. The methodology developed a new software-based index namely Institutional Environmental Health Index (IEHI) that was integrated from ontology-based Multi-Criteria Group Decision-Making models based on the principles of fuzzy modeling and consensus evaluation. Fuzzy Ordered Weighting Average (OWA) with the capability of modeling the uncertainties and decision-making risks along with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were employed as the computation engine. The performance of the extended index was examined through an applied example on 20 mosques as public institutions. IEHI could analyze big data collected by environmental health investigators and convert them to a single and interpretable number. The index detected the mosques with very unsuitable health conditions that should be in priority of sanitation and suitable ones as well. Due to the capability of defining the type and numbers of criteria and benefitting from specific and user-friendly software namely Group Fuzzy Decision-Making, this index is highly flexible and practical. The methodology could be used for numerating the environmental health conditions in any intended institution or occupation. The proposed index would provide e-health assessment by more efficient analysis of big data and risks that make more realistic decisions in environmental health system.