A Tool for Aligning Event Logs and Prescriptive Process Models through Automated Planning

In Conformance Checking, alignment is the problem of detecting and repairing nonconformity between the actual execution of a business process, as recorded in an event log, and the model of the same process. Literature proposes solutions for the alignment problem that are implementations of planning algorithms built ad-hoc for the specific problem. Unfortunately, in the era of big data, these ad-hoc implementations do not scale sufficiently compared with well-established planning systems. In this paper, we tackle the above issue by presenting a tool, also available in ProM, to represent instances of the alignment problem as automated planning problems in PDDL (Planning Domain Definition Language) for which state-of-the-art planners can find a correct solution in a finite amount of time. If alignment problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with advantages in term of versatility and customization. Furthermore, by employing several processes and event logs of different sizes, we show how our tool outperforms existing approaches of several order of magnitude and, in certain cases, carries out the task while existing approaches run out of memory

Alignment-based conformance checking if hierarchical process models

Process mining has received much attention in the field of business pro cess management. Event logs that are generated from information systems can be correlated with the process models for conformance checking. The process models describe event activities at an abstraction level. However, hierarchical business pro cesses, as a kind of typical complex process scenario, describe sub-processes invoca tion and multi-instantiation patterns. As existing conformance checking approaches cannot identify sub-processes within hierarchical process models. They cannot be used for conformance checking of hierarchical process models. To handle this limi tation, a definition of hierarchically alignment sequences is presented in this paper. Meanwhile, a novel conformance checking approach for hierarchical process models and event logs is proposed. The proposed method has been implemented within the ProM toolkit, which is an open-source process mining software. To evaluate the effectiveness of the proposed approach, both artificial and real-world event logs are utilized in a comparative analysis against existing state-of-the-art approaches

Process Mining applied to BPMN-E2

Dissertação de mestrado em Engenharia InformáticaProcess Mining is characterized by a group of techniques that aim to mine and analyze event logs in an effort to extract patterns and useful insights regarding a business process, allowing for a better and more efficient understanding of it. This topic is sparking increasing interest in both academia and business contexts, which results in fast advances in the algorithms being applied, as well as in the subjacent notations used for process modeling. One of the most used notations for process modeling is Business Processs Model and Notation (BPMN), being its expressiveness in representing processes its strongest attribute. However, this notation reveals some flaws when dealing with some specific contexts, struggling to model activity duration, quality control and activity effects in context-specific resources. For this particular purpose, an extension named Business Processs Model and Notation Extended Expressiveness (BPMN-E2 ) was developed to tackle the limitations found on the original notation. In this dissertation, a new conformance checking algorithm was developed focusing on finding non-conformities between an event log and process models, taking into consideration the new elements that BPMN-E2 has to offer. Fuelled by a few setbacks found during this work, an event log clustering technique was also developed to downsize large event logs without stripping its representativity. Furthermore, the BPMN-E2 notation was used to model a real-life process and the developed conformance checking algorithm was applied to illustrate its analytical potential.Process Mining caracteriza um conjunto de técnicas que permitem a mineração e análise de event logs com o principal objetivo de extrair destes padrões e informações relevantes que permitam uma melhor percepção e eficiência dos processos realizados num determinado contexto. Esta área tem verificado um interesse crescente, tanto em meio académico como em meio empresarial, sendo notados avanços quer nos algoritmos de mineração utilizados, quer nas notações subjacentes utilizadas para modelar processos. Uma das notações mais utilizadas por profissionais e académicos é o Business Processs Model and Notation (BPMN) devido à sua expressividade na representação de processos. No entanto, esta mesma notação apresenta alguns inconvenientes quando é usada em determinados contextos, sendo difícil representar, por exemplo, durações de atividades, controlo de qualidade e efeitos da atividade nas características de um produto. Num esforço para resolver estes problemas, foi desenvolvida uma extensão chamada Business Processs Model and Notation Extended Expressiveness (BPMN-E2 ). Neste projeto foi desenvolvido um novo algoritmo de conformance checking, tendo em consideração a informação complementar oferecida pelo BPMN-E2 . Motivada por alguns contratempos durante o trabalho, uma técnica de clustering foi também desenvolvida para reduzir o tamanho de event logs sem afetar a sua representatividade. A notação BPMN-E2 foi também usada para modelar um processo real e o algoritmo de conformance checking usado nesse contexto para ilustrar o seu potencial analítico

Artifact Lifecycle Discovery

Artifact-centric modeling is a promising approach for modeling business processes based on the so-called business artifacts - key entities driving the company's operations and whose lifecycles define the overall business process. While artifact-centric modeling shows significant advantages, the overwhelming majority of existing process mining methods cannot be applied (directly) as they are tailored to discover monolithic process models. This paper addresses the problem by proposing a chain of methods that can be applied to discover artifact lifecycle models in Guard-Stage-Milestone notation. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) process discovery and analysis methods can be reused in a flexible manner. The methods presented in this paper are implemented as software plug-ins for ProM, a generic open-source framework and architecture for implementing process mining tools

Subgraph Mining for Anomalous Pattern Discovery in Event Logs

Conformance checking allows organizations to verify whether their IT system complies with the prescribed behavior by comparing process executions recorded by the IT system against a process model (representing the normative behavior). However, most of the existing techniques are only able to identify low-level deviations, which provide a scarce support to investigate what actually happened when a process execution deviates from the specification. In this work, we introduce an approach to extract recurrent deviations from historical logging data and generate anomalous patterns representing high-level deviations. These patterns provide analysts with a valuable aid for investigating nonconforming behaviors; moreover, they can be exploited to detect high-level deviations during conformance checking. To identify anomalous behaviors from historical logging data, we apply frequent subgraph mining techniques together with an ad-hoc conformance checking technique. Anomalous patterns are then derived by applying frequent items algorithms to determine highly-correlated deviations, among which ordering relations are inferred. The approach has been validated by means of a set of experiments

Discovering duplicate tasks in transition systems for the simplification of process models

This work presents a set of methods to improve the understandability of process models. Traditionally, simplification methods trade off quality metrics, such as fitness or precision. Conversely, the methods proposed in this paper produce simplified models while preserving or even increasing fidelity metrics. The first problem addressed in the paper is the discovery of duplicate tasks. A new method is proposed that avoids overfitting by working on the transition system generated by the log. The method is able to discover duplicate tasks even in the presence of concurrency and choice. The second problem is the structural simplification of the model by identifying optional and repetitive tasks. The tasks are substituted by annotated events that allow the removal of silent tasks and reduce the complexity of the model. An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.Peer ReviewedPostprint (author's final draft