PMLAB: An scripting environment for process mining

In a decade of process mining research, several algorithms have been proposed to solve particular process mining tasks. At the same pace, tools have appeared both in the academic and the commercial domains. These tools have enabled the use of process mining practices to a rather limited extent. In this paper we advocate for a change in the mentality: process mining may be an exploratory discipline, and PMLAB - a Python-based scripting environment supporting this - is proposed. This demo presents the main features of the PMLAB environment.Peer ReviewedPostprint (published version

On the realization of reactive systems

A new notion of realization of reactive systems is defined. Realization is defined as a relation between the states of two transition systems, the specification and the implementation, in which events are classified as input, output or internal. This new definition attempts to model the correct interaction between a system and its environment. The differences with other definitions of refinement and realization are discussed.Postprint (published version

The alignment of formal, structured and unstructured process descriptions

Nowadays organizations are experimenting a drift on the way processes are managed. On the one hand, formal notations like Petri nets or Business Process Model and Notation (BPMN) enable the unambiguous reasoning and automation of designed processes. This way of eliciting processes by manual design, which stemmed decades ago, will still be an important actor in the future. On the other hand, regulations require organizations to store their process executions in structured representations, so that they are known and can be analyzed. Finally, due to the different nature of stakeholders within an organization (ranging from the most technical members, e.g., developers, to less technical), textual descriptions of processes are also maintained to enable that everyone in the organization understands their processes. In this paper I will describe techniques for facilitating the interconnection between these three process representations. This requires interdisciplinary research to connect several fields: business process management, formal methods, natural language processing and process mining.Peer ReviewedPostprint (author's final draft

Decomposed process discovery and conformance checking

Decomposed process discovery and decomposed conformance checking are the corresponding variants of the two monolithic fundamental problems in process mining (van der Aalst 2011): automated process discovery, which considers the problem of discovering a process model from an event log (Leemans 2009), and conformance checking, which addresses the problem of analyzing the adequacy of a process model with respect to observed behavior (Munoz-Gama 2009), respectively. The term decomposed in the two definitions is mainly describing the way the two problems are tackled operationally, to face their computational complexity by splitting the initial problem into smaller problems, that can be solved individually and often more efficiently.Postprint (author's final draft

Security-sensitive tackling of obstructed workflow executions

Imposing access control onto workflows considerably reduces the set of users authorized to execute the workflow tasks. Further constraints (e.g. Separation of Duties) as well as unexpected unavailabilty of users may finally obstruct the successful workflow execution. To still complete the execution of an obstructed workflow, we envisage a hybrid approach. If a log is provided, we partition its traces into “successful” and “obstructed” ones by analysing the given workflow and its authorizations. An obstruction should then be solved by finding its nearest match from the list of successful traces. If no log is provided, we flatten the workflow and its authorizations into a Petri net and encode the obstruction with a corresponding “obstruction marking”. The structural theory of Petri nets shall then be tweaked to provide a minimized Parikh vector, that may violate given firing rules, however reach a complete marking and by that, complete the workflow.Peer ReviewedPostprint (published version

Process discovery algorithms using numerical abstract domains

The discovery of process models from event logs has emerged as one of the crucial problems for enabling the continuous support in the life-cycle of an information system. However, in a decade of process discovery research, the algorithms and tools that have appeared are known to have strong limitations in several dimensions. The size of the logs and the formal properties of the model discovered are the two main challenges nowadays. In this paper we propose the use of numerical abstract domains for tackling these two problems, for the particular case of the discovery of Petri nets. First, numerical abstract domains enable the discovery of general process models, requiring no knowledge (e.g., the bound of the Petri net to derive) for the discovery algorithm. Second, by using divide and conquer techniques we are able to control the size of the process discovery problems. The methods proposed in this paper have been implemented in a prototype tool and experiments are reported illustrating the significance of this fresh view of the process discovery problem.Peer ReviewedPostprint (author’s final draft

Structural computation of alignments of business processes over partial orders

Relating event data and process models is becoming an important element for organizations. This paper presents a novel approach for aligning traces and process models. The approach is based on the structural theory of Petri nets (the marking equation), applied over an unfolding of the initial process model. Given an observed trace, the approach adopts an iterative optimization mechanism on top of the unfolding, computing at each iteration part of the resulting alignment. In contrast to the previous work that is primarily grounded in the marking equation, this approach is guaranteed to provide real solutions, and tries to mimic as much as possible the events observed in the trace. Experiments witness the significance of this approach both in quality and execution time perspectives.Peer ReviewedPostprint (author's final draft

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

A High-level strategy for C-net discovery

Causal nets have been recently proposed as a suitable model for process mining, due to their declarative semantics and compact representation. However, the discovery of causal nets from a log is a complex problem. The current algorithmic support for the discovery of causal nets comprises either fast but inaccurate methods (compromising quality), or accurate algorithms that are computational demanding, thus limiting the size of the inputs they can process. In this paper a high-level strategy is presented, which uses appropriate clustering techniques to split the log into pieces, and benefits from the additive nature of causal nets. This allows amalgamating structurally the discovered Causal net of each piece to derive a valuable model. The claims in this paper are accompanied with experimental results showing the significance of the high-level strategy presented.Postprint (published version

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