An overview of semantic-based process mining techniques: trends and future directions

Abstract

Process mining algorithms essentially reflect the execution behavior of events in an event log for conformance checking, model discovery, or enhancement. Domain experts have developed several process mining algorithms based on theoretical frameworks such as linear integer programming, heuristics, and genetic algorithms, region-based and semantic-based approaches. The idea is to generate insightful representations of these processes of information systems to enable process mining practitioners to gain insight into their systems. Recently, there has been a shift toward semantic-based approaches for process mining since they not only discover enhanced models but also emphasize context. To this effect, this paper conducts a comprehensive review of 30 articles on semantic process mining techniques. It was found that 44.7% of all works used semantics for process discovery, 23.7% for model enhancement, and conformance checking was the least with 10.5%. We further indicate the benefits and contributions of these methods to process mining. Challenges, opportunities, and prospective future research areas are also discussed.