Process-aware Systems Research Articles

Using process mining for workarounds analysis in context: Learning from a small and medium-sized company case

Workarounds are deviations in the execution of designed, de jure, work processes. Process mining research has developed methods for unobtrusive workaround analysis using process-aware systems’ datasets. This study applies process mining for workaround analysis in a medium-sized enterprise (SME). SME contexts can be challenging for workaround mining because SMEs often lack de jure process designs and their process-supportive information systems may have ambiguous semantics. The identification of de jure models and the solving of systems data ambiguities are the first steps in workarounds identification. A semantically well-defined information system may enable factual, de facto, process mining. Comparing the de jure and de facto process models may give candidate workarounds. Our study shows that (1) incomplete de jure models hinder the use of process mining for detecting workarounds, and (2) human interpretation of process mining outcomes is needed to realize a useful triple loop organizational learning from workarounds mining.

NewWave: Workflow engine

• Pharo. • Workflow engine. • NewWave Pharo project. • Business process management. • Business process modeling. An ever-increasing demand for information systems in the last few decades brought many new opportunities but also presented new challenges for companies in the landscape of doing business globally. In these terms, software systems need to adapt almost instantaneously to new requirements, opportunities, and customer expectations. Process-aware systems have become integral part in business information system solutions, finding their place at different locations. This paper presents a workflow engine - NewWave , created with the goal to enable an extensible workflow management system, to facilitate easy specification and implementation of application business logic with focus on simplicity and immediate feedback. Through seamless integration with Pharo applications, libraries, tools, and leveraging Pharo's live environment, the NewWave enables users to create dynamically adaptable process models to facilitate the flexible adaptation of business logic.

Investigating the success of operational business process management systems

Business process management systems (BPMS) belong to a class of enterprise information systems that are characterized by the dependence on explicitly modeled process logic. Through the process logic, it is relatively easy to manage explicitly the routing and allocation of work items along a business process through the system. Inspired by the DeLone and McLean framework, we theorize that these process-aware system features are important attributes of system quality, which in turn will elevate key user evaluations such as perceived usefulness, and usage satisfaction. We examine this theoretical model using data collected from four different, mostly mature BPM system projects. Our findings validate the importance of input quality as well as allocation and routing attributes as antecedents of system quality, which, in turn, determines both usefulness and satisfaction with the system. We further demonstrate how service quality and workflow dependency are significant precursors to perceived usefulness. Our results suggest the appropriateness of a multi-dimensional conception of system quality for future research, and provide important design-oriented advice for the design and configuration of BPMSs.

Algorithms for anomaly detection of traces in logs of process aware information systems

This paper discusses four algorithms for detecting anomalies in logs of process aware systems. One of the algorithms only marks as potential anomalies traces that are infrequent in the log. The other three algorithms: threshold, iterative and sampling are based on mining a process model from the log, or a subset of it. The algorithms were evaluated on a set of 1500 artificial logs, with different profiles on the number of anomalous traces and the number of times each anomalous traces was present in the log. The sampling algorithm proved to be the most effective solution. We also applied the algorithm to a real log, and compared the resulting detected anomalous traces with the ones detected by a different procedure that relies on manual choices.

Fraud detection in process aware systems

In the last years, some large companies have been involved in scandals related to financial mismanagement, which represented a large financial damage to their stockholders. To recover market confidence, certifications for best practices of governance were developed, and in some cases, harder laws were implemented. Companies adhered to these changes as a response to the market, deploying process aware systems (PAS) and adopting the best practices of governance. However, companies demand a rapid response to strategic changes or changes in business models between partners, which may impose serious drawbacks to the adoption of normative PAS to the competitiveness of these companies. Thus, while companies need flexible PAS, flexibility may compromise security. To re-balance the trade-off between security and flexibility, we present in this work an anomaly detection algorithm for PAS. The identification of anomalous events can help the adoption of flexible PAS without the loss of security properties.

Genetic-based Anomaly Detection in Logs of Process Aware Systems

Genetic-based Anomaly Detection in Logs of Process Aware Systems