Semantic-Based Model Analysis Towards Enhancing Information Values of Process Mining: Case Study of Learning Process Domain

Abstract

Process mining results can be enhanced by adding semantic knowledge to the derived models. Information discovered due to semantic enrichment of the deployed process models can be used to lift process analysis from syntactic level to a more conceptual level. The work in this paper corroborates that semantic-based process mining is a useful technique towards improving the information value of derived models from the large volume of event logs about any process domain. We use a case study of learning process to illustrate this notion. Our goal 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 mapping of the resulting learning model derived from mining event data about a learning 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. The semantic analysis allows the meaning of the learning objects to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are used to determine useful learning patterns by means of the Semantic Learning Process Mining (SLPM) algorithm -technically described as Semantic-Fuzzy Miner. To this end, we show how data from learning processes are being extracted, semantically prepared, and transformed into mining executable formats to enable prediction of individual learning patterns through further semantic analysis of the discovered models.

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

“…A number of researchers have directed their work towards the use and application of this new advanced aspect of PM within the educational settings [22] [22] EPM emerges from the educational data mining (EDM) discipline, and the drive for its incentive is primarily to discover, analyse and improve the educational processes based on the hidden or unobserved informations within the events log recorded by the IT systems. References [4] and [22] are even more specific about the existing methods for extracting models within the EPM which are limited to some extent because the approaches depend on traditional PM techniques that are purely syntactic in nature (i.e. based on labels in event logs) to discover process models.…”

“…In Phase 1: the PM technique is applied in order to make available the process mappings for the learning process, and check its conformance with the event logs based on the Fuzzy Miner [27] as described in [4] and [6]. The main reason is that the resulting process map allows us to quickly, and interactively explore the processes into multiple directions and to show the individual activities workflow, and then provide platform for semantic annotation of the different process elements within the knowledge base.…”

“…According to the work of [1] - [4] the efficiency or quality of the so-called organizations process can be improved if the process analysts and/or owners could perform an accurate analysis or exploration of the extracted event logs. For instance, the process of visualizing the relations or attributes the process instances share amongst themselves in a knowledge base.…”

Ontology: Core Process Mining and Querying Enabling Tool

“…Perhaps, according to the works in Refs. [1][2][3][4] an accurate exploration or analysis of the extracted events log is capable of providing vital and valuable information with regards to the quality of support being offered for the so-called organizations and their information knowledge-base or system at large. For example, revealing the underlying relationships the process elements or resources share amongst themselves within the information knowledge-base.…”

“…In turn, the conceptual method of analysis provides an easy way to analyse the datasets (i.e. the event logs and models), and even more allows the meaning of the process elements to be enhanced through the use of property descriptions languages or syntax-such as the Ontology Web-Rule Language (OWL) [7] Semantic Web Rule Language (SWRL) [8], Description Logic (DL) queries [9], and classification of discoverable entities or taxonomy [4] in order to make available inference knowledge that could be utilized to determine useful patterns by means of the semantic reasoning aptitudes. On the other hand, the semantic modelling (ontological representations) and analysis techniques provide us with the opportunity to develop intelligent algorithms and tools which are capable of enhancing the resulting process models through explicit specification of the concepts (often referred to as conceptualisation) [5,10,11] in order to identify appropriate domain semantics and relationships amongst the process elements.…”