Leveraging Natural Language Processing for Business Process Mining from Unstructured Data Sources

Abstract

This research explores the integration of Natural Language Processing (NLP) within Business Process Mining (BPM) to enhance the analysis of unstructured data sources, such as emails, documents, and customer interactions. Traditionally, BPM has relied heavily on structured data, limiting its applicability in contexts where valuable process-related information is embedded in unstructured formats. By leveraging NLP techniques, this paper investigates how textual data can be transformed into actionable insights for process analysis, specifically focusing on customer service and administrative processes. The study outlines key methodologies, including entity recognition, sentiment analysis, and topic modeling, to extract relevant process-related information from unstructured data streams. Additionally, the paper addresses the challenges in automating these processes, such as the complexities of natural language understanding, context recognition, and data integration. It emphasizes the potential for enhancing process discovery, conformance checking, and performance analysis by incorporating unstructured data. The research contributes to expanding the scope of BPM by introducing a new paradigm for extracting meaningful process insights from diverse data types. This methodology is expected to significantly improve decision-making and operational efficiency in business environments, particularly in sectors that rely on large volumes of text-based data for process execution.