A Framework for Online Conformance Checking

Conformance checking – a branch of process mining – focuses on establishing to what extent actual executions of a process are in line with the expected behavior of a reference model. Current conformance checking techniques only allow for a-posteriori analysis: the amount of (non-)conformant behavior is quantified after the completion of the process instance. In this paper we propose a framework for online conformance checking: not only do we quantify (non-)conformant behavior as the execution is running, we also restrict the computation to constant time complexity per event analyzed, thus enabling the online analysis of a stream of events. The framework is instantiated with ideas coming from the theory of regions, and state similarity. An implementation is available in ProM and promising results have been obtained.Peer ReviewedPostprint (author's final draft

Sensing distress – towards a blended method for detecting and responding to problematic customer experience events

Excellent Customer Experience (CE) is a strategic priority for many large service organisations in a competitive marketplace. CE should be seamless, and in most cases it is, with customers ordering, paying for and receiving services that align with their expectations. However, in rare cases, an exceptional process event leads to service delivery delay or failure, and both the customer and organ-isation end up in complex recovery situations as a result. Unless this recovery is handled effectively inefficiency, avoidable costs and brand damage can result. So how can organisations sense when these problems are occurring and how can they respond to avoid these negative consequences? Our paper proposes a blended methodology where process mining and qualitative user research com-bine to give a holistic picture of customer experience issues, derived from a par-ticular customer case study. We propose a theoretical model for detecting and responding to customer issues, and discuss the challenges and opportunities of such a model when applied in practice in large service organisations

Clinical Processes - The Killer Application for Constraint-Based Process Interactions?

For more than a decade, the interest in aligning information systems in a process-oriented way has been increasing. To enable operational support for business processes, the latter are usually specified in an imperative way. The resulting process models, however, tend to be too rigid to meet the flexibility demands of the actors involved. Declarative process modeling languages, in turn, provide a promising alternative in scenarios in which a high level of flexibility is demanded. In the scientific literature, declarative languages have been used for modeling rather simple processes or synthetic examples. However, to the best of our knowledge, they have not been used to model complex, real-world scenarios that comprise constraints going beyond control-flow. In this paper, we propose the use of a declarative language for modeling a sophisticated healthcare process scenario from the real world. The scenario is subject to complex temporal constraints and entails the need for coordinating the constraint-based interactions among the processes related to a patient treatment process. As demonstrated in this work, the selected real process scenario can be suitably modeled through a declarative approach.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

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

Ideas del alumnado de primaria y secundaria sobre aleatoriedad

En este trabajo comparamos las características de las secuencias de resultados aleatorios y distribuciones aleatorias de puntos generadas por tres grupos de alumnos de Educación Primaria y Secundaria con las propiedades matemáticas de las mismas. La finalidad es describir el significado personal que dichos estudiantes asignan a la aleatoriedad.In this paper we compare the features in random sequences and random distribution of points produced by three groups of students in Primary and Secondary Education with the mathematical properties of the same. The aim is to describe the meaning of randomness for these students.Facultad de Educación y Humanidades - Campus de Melilla (Universidad de Granada)Este trabajo es parte de los proyectos SEJ2004-00789, Madrid, MCYT y FQM-126, Junta de Andalucí

The biggest business process management problems to solve before we die

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one’s field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology

Who is behind the Model? Classifying Modelers based on Pragmatic Model Features

\u3cp\u3eProcess modeling tools typically aid end users in generic, non-personalized ways. However, it is well conceivable that different types of end users may profit from different types of modeling support. In this paper, we propose an approach based on machine learning that is able to classify modelers regarding their expertise while they are creating a process model. To do so, it takes into account pragmatic features of the model under development. The proposed approach is fully automatic, unobtrusive, tool independent, and based on objective measures. An evaluation based on two data sets resulted in a prediction performance of around 90%. Our results further show that all features can be efficiently calculated, which makes the approach applicable to online settings like adaptive modeling environments. In this way, this work contributes to improving the performance of process modelers.\u3c/p\u3