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

Machine Learning-Based Framework for Log-Lifting in Business Process Mining Applications

Real-life event logs are typically much less structured and more complex than the predefined business activities they refer to. Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and events recorded during process execution. Unfortunately, event logs and process model activities are defined at different levels of granularity. The challenges posed by this discrepancy can be addressed by means of log-lifting. In this work, we develop a machine-learning-based framework aimed at bridging the abstraction level gap between logs and process models. The proposed framework operates in two main phases: log segmentation and machine-learning-based classification. The purpose of the segmentation phase is to identify the potential segment separators in a flow of low-level events, in which each segment corresponds to an unknown high-level activity. For this, we propose a segmentation algorithm based on maximum likelihood with n-gram analysis. In the second phase, event segments are mapped into their corresponding high-level activities using a supervised machine learning technique. Several machine learning classification methods are explored including ANNs, SVMs, and random forest. We demonstrate the applicability of our framework using a real-life event log provided by the SAP company. The results obtained show that a machine learning approach based on the random forest algorithm outperforms the other methods with an accuracy of 96.4%. The testing time was found to be around 0.01s, which makes the algorithm a good candidate for real-time deployment scenarios