Automatic Process Model Discovery from Textual Methodologies: An Archaeology Case Study

Ninth IEEE International Conference on Research Challenges in Information Science (RCIS 2015)

Atenas. Grecia
Congreso. 2015

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.

Process mining. Process mining technique. Natural language processing. Process model. Technical action research.