Using Semantic-based Approach to Manage Perspectives of Process Mining: Application on Improving Learning Process Domain Data

International audienceMining useful knowledge from data readily available in today's information systems has been a common challenge in recent years as more and more events are being recorded, and there is need to improve and support many organisational processes in a competitive and rapidly changing environments. The work in this paper shows using a case study of Learning Process - how data from various process domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time processes. In so doing, it enables the prediction of individual patterns/behaviour through further semantic analysis of the discovered models. Our aim is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved process analysis of the captured data. The approach involves augmenting the informative value of the resulting model derived from mining event data about the process by semantically annotating the process elements with concepts they represent in real time using process descriptions languages, and linking them to an ontology specifically designed for representing learning processes to allow for the analysis of the extracted event logs based on concepts rather than the event tags of the process. The semantic analysis allows the meaning of the learning object properties and model to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are then used to determine useful learning patterns by means of the proposed Semantic Learning Process Mining (SLPM) formalization described technically as Semantic-Fuzzy Miner. As a result, the approach provides us with the capability to infer new and discover hidden relationships/attributes the process instances share amongst themselves within the knowledge base, and the ability to identify and address the problem of determining the presence of different learning patterns or behaviour. Inference knowledge discovered due to semantic enrichment of the process model is advantageous especially in solving some didactic issues and answering some questions with regards to different Learners behaviour within the context of process mining and semantic model analysis. To this end, we show that information derived from process mining algorithms can be improved by adding semantic knowledge to the resulting model