The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain

The process mining (PM) field combines techniques
from computational intelligence which has been lately considered
to encompass artificial intelligence (AI) or even the latter,
augmented intelligence (AIs) systems, and the data mining (DM)
to process modelling in order to analyze event logs. To this end, this
paper presents a semantic-based process mining framework
(SPMaAF) that exhibits high level of accuracy and conceptual
reasoning capabilities particularly with its application in real
world settings. The proposed framework proves useful towards
the extraction, semantic preparation, and transformation of
events log from any domain process into minable executable
formats – with focus on supporting the further process of
discovering, monitoring and improvement of the extracted
processes through semantic-based analysis of the discovered
models. Practically, the implementation of the proposed
framework demonstrates the main contribution of this paper; as
it presents a Semantic-Fuzzy mining approach that makes use of
labels (i.e. concepts) within event logs about a domain process
using a case study of the Learning Process. The paper provides a
method which aims to allow for mining and improved analysis of
the resulting process models through semantic – labelling
(annotation), representation (ontology) and reasoning (reasoner).
Consequently, the series of experimentations and semantically
motivated algorithms shows that the proposed framework and its
main application in real-world has the capacity of enhancing the
PM results or outcomes from the syntactic to a much more
abstraction levels.

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