Automatic process model discovery from textual methodologies

Abstract

Process mining has been successfully used in automatic knowledge discovery and in providing guidance or support. The known process mining approaches rely on processes being executed with the help of information systems thus enabling the automatic capture of process traces as event logs. However, there are many other fields such as Humanities, Social Sciences and Medicine where workers follow processes and log their execution manually in textual forms instead. The problem we tackle in this paper is mining process instance models from unstructured, text-based process traces. Using natural language processing with a focus on the verb semantics, we created a novel unsupervised technique TextProcessMiner that discovers process instance models in two steps: 1.ActivityMiner mines the process activities; 2.ActivityRelationshipMiner mines the sequence, parallelism and mutual exclusion relationships between activities. We employed technical action research through which we validated and preliminarily evaluated our proposed technique in an Archaeology case. The results are very satisfactory with 88% correctly discovered activities in the log and a process instance model that adequately reflected the original process. Moreover, the technique we created emerged as domain independent.

User Requirement Solicitation for an Information Retrieval System Applied to Dutch Grey Literature in the Archaeology Domain

“…Some experiments have been carried out in text mining in archaeology, across multiple countries and languages. This includes work by Epure et al (2015), describing how to mine process models from natural text, and also recent works by Øyvind and Martin-Rodilla presented at EAA 2018, describing automatic information extraction from reports and semiautomatic analysis on heritage related legal texts, respectively (currently unpublished). Related work is also carried out in other disciplines such as history, a notable example being ALCIDE, a system that extracts and visualises content from large document collections in the history domain (Moretti et al 2016).…”

Comparing and Aligning Process Representations

“…These approaches elicit process models from different kinds of texts. Some focus on specific text types, such as the generation of models from use cases [253], group stories [111], or methodological descriptions [90], while others take general textual process descriptions as input [99,108]. These techniques combine extensive use of NLP tools with specifically tailored analysis techniques, in order to extract process-related information from natural language texts.…”

Recommendations for enhancing the usability and understandability of process mining in healthcare

“…Important data about the execution of the process, such as case/event attributes, can be embedded in such free text fields. To unearth insights from unstructured textual data to consider it for process mining purposes, research efforts at the boundary between natural language processing and process mining are required [135,136]. Within the context of healthcare processes, efforts such as Najafabadipour et al [134],…”