A NLP-Oriented Methodology to Enhance Event Log Quality

Abstract

The quality of event logs is a crucial cornerstone for the feasibility of the application of later process mining techniques. The wide variety of data that can be included in an event log refer to information about the activity, such as what, who or where. In this paper, we focus on event logs that include textual information written in a natural language that contains exhaustive descriptions of activity executions. In this context, a pre-processing step is necessary since textual information is unstructured and it can contain inaccuracies that will provoke the impracticability of process mining techniques. For this reason, we propose a methodology that applies Natural Language Processing (NLP) to raw event log by relabelling activities. The approach let the customised description of the measurement and assessment of the event log quality depending on expert requirements. Additionally, it guides the selection of the most suitable NLP techniques for use depending on the event log. The methodology has been evaluated using a real-life event log that includes detailed textual descriptions to capture the management of incidents in the aircraft assembly process in aerospace manufacturing.

Event Log Data Quality Issues and Solutions

“…Natural language processing (NLP) is a subfield of machine learning closely related to artificial intelligence, enabling machines to understand human language. In the process mining preprocessing step, NLP can be applied to detect imprecise events and activity labels and relabel them [21] or remove redundant labels [22]. Clustering is also a machine learning technique that analyzes patterns and relationships in a data set to identify similar groups or clusters.…”