Amending C-net discovery algorithms

As the complexity of information systems evolves, there is a growing interest in defining suitable process models than can overcome the limitations of traditional formalisms like Petri nets or related. Causal nets may be one of such promising process models, since important characteristics of their semantics deviate from the ones in the literature. Due to their novelty, very few discovery algorithms exist for Causal nets. Moreover, the existing ones offer very few guarantees regarding the outcome produced. This paper describes an algorithm that can be applied as a second step to any discovery technique to significantly improve the quality of the final Causal net derived. We have tested the technique in combination with the existing algorithms in the literature on several benchmarks, noticing a considerable improvement in all of them.Postprint (published version

Encoding process discovery problems in SMT

Information systems, which are responsible for driving many processes in our lives (health care, the web, municipalities, commerce and business, among others), store information in the form of logs which is often left unused. Process mining, a discipline in between data mining and software engineering, proposes tailored algorithms to exploit the information stored in a log, in order to reason about the processes underlying an information system. A key challenge in process mining is discovery: Given a log, derive a formal process model that can be used afterward for a formal analysis. In this paper, we provide a general approach based on satisfiability modulo theories (SMT) as a solution for this challenging problem. By encoding the problem into the logical/arithmetic domains and using modern SMT engines, it is shown how two separate families of process models can be discovered. The theory of this paper is accompanied with a tool, and experimental results witness the significance of this novel view of the process discovery problem.Peer ReviewedPostprint (author's final draft

Amending C-net discovery algorithms

As the complexity of information systems evolves, there is a growing interest in defining suitable process models than can overcome the limitations of traditional formalisms like Petri nets or related. Causal nets may be one of such promising process models, since important characteristics of their semantics deviate from the ones in the literature. Due to their novelty, very few discovery algorithms exist for Causal nets. Moreover, the existing ones offer very few guarantees regarding the outcome produced. This paper describes an algorithm that can be applied as a second step to any discovery technique to significantly improve the quality of the final Causal net derived. We have tested the technique in combination with the existing algorithms in the literature on several benchmarks, noticing a considerable improvement in all of them

Amending C-net discovery algorithms

As the complexity of information systems evolves, there is a growing interest in defining suitable process models than can overcome the limitations of traditional formalisms like Petri nets or related. Causal nets may be one of such promising process models, since important characteristics of their semantics deviate from the ones in the literature. Due to their novelty, very few discovery algorithms exist for Causal nets. Moreover, the existing ones offer very few guarantees regarding the outcome produced. This paper describes an algorithm that can be applied as a second step to any discovery technique to significantly improve the quality of the final Causal net derived. We have tested the technique in combination with the existing algorithms in the literature on several benchmarks, noticing a considerable improvement in all of them