Unfolding-Based Process Discovery

This paper presents a novel technique for process discovery. In contrast to the current trend, which only considers an event log for discovering a process model, we assume two additional inputs: an independence relation on the set of logged activities, and a collection of negative traces. After deriving an intermediate net unfolding from them, we perform a controlled folding giving rise to a Petri net which contains both the input log and all independence-equivalent traces arising from it. Remarkably, the derived Petri net cannot execute any trace from the negative collection. The entire chain of transformations is fully automated. A tool has been developed and experimental results are provided that witness the significance of the contribution of this paper.Comment: This is the unabridged version of a paper with the same title appearead at the proceedings of ATVA 201

Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Verification of Logs - Revealing Faulty Processes of a Medical Laboratory

Abstract. If there is a suspicion of Lyme disease, a blood sample of a patient is sent to a medical laboratory. The laboratory performs a number of dierent blood examinations testing for antibodies against the Lyme disease bacteria. The total number of dierent examinations depends on the intermediate results of the blood count. The costs of each examination is paid by the health insurance company of the patient. To control and restrict the number of performed examinations the health insurance companies provide a charges regulation document. If a health insurance company disagrees with the charges of a laboratory it is the job of the public prosecution service to validate the charges according to the regulation document. In this paper we present a case study showing a systematic approach to reveal faulty processes of a medical laboratory. First, files produced by the information system of the respective laboratory are analysed and consolidated in a database. An excerpt from this database is translated into an event log describing a sequential language of events performed by the information system. With the help of the regulation document this language can be split in two sets- the set of valid and the set of faulty words. In a next step, we build a coloured Petri net model corre-sponding to the set of valid words in a sense that only the valid words are executable in the Petri net model. In a last step we translated the coloured Petri net into a PL/SQL-program. This program can automat-ically reveal all faulty processes stored in the database.

A symbolic algorithm for the synthesis of bounded Petri nets

This paper presents an algorithm for the synthesis of bounded Petri nets from transition systems. A bounded Petri net is always provided in case it exists. Otherwise, the events are split into several transitions to guarantee the synthesis of a Petri net with bisimilar behavior. The algorithm uses symbolic representations of multisets of states to efficiently generate all the minimal regions. The algorithm has been implemented in a tool. Experimental results show a significant net reduction when compared with approaches for the synthesis of safe Petri nets.Peer ReviewedPostprint (author's final draft

An SMT-based discovery algorithm for C-nets

Recently, Causal nets have been proposed as a suitable model for process discovery, due to their declarative semantics and the great expressiveness they possess. In this paper we propose an algorithm to discover a causal net from a set of traces. It is based on encoding the problem as a Satisfiability Modulo Theories (SMT) formula, and uses a binary search strategy to optimize the derived model. The method has been implemented in a prototype tool that interacts with an SMT solver. The experimental results obtained witness the capability of the approach to discover complex behavior in limited time.Postprint (published version

Process mining meets abstract interpretation

The discovery of process models out of system traces is an interesting problem that has received significant attention in the last years. In this work, a theory for the derivation of a Petri net from a set of traces is presented. The method is based on the theory of abstract interpretation, which has been applied successfully in other areas. The principal application of the theory presented is Process Mining, an area that tries to incorporate the use of formal models both in the design and use of information systems.Postprint (published version

Discovery of frequent episodes in event logs

Lion's share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent itemset mining, association rule learning, sequence mining, and traditional episode mining) do not consider process instances. An episode is a collection of partially ordered events. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach