Abstract. Workflow nets, a particular class of Petri nets, have become one of the standard ways to model and analyze workflows. Typically, they are used as an abstraction of the workflow that is used to check the so-called soundness property. This property guarantees the absence of livelocks, deadlocks, and other anomalies that can be detected without domain knowledge. Several authors have proposed alternative notions of soundness and have suggested to use more expressive languages, e.g., models with cancellations or priorities. This paper provides an overview of the different notions of soundness and investigates these in the presence of different extensions of workflow nets. We will show that the eight soundness notions described in the literature are decidable for workflow nets. However, most extensions will make all of these notions undecidable. These new results show the theoretical limits of workflow verification. Moreover, we discuss some of the analysis approaches described in the literature.

Abstract. The degree of flexibility of workflow management systems heavily influences the way business processes are executed. Constraint-based models are considered to be more flexible than traditional models because of their semantics: everything that does not violate constraints is allowed. Although constraint-based models are flexible, changes to process definitions might be needed to comply with evolving business domains and exceptional situations. Flexibility can be increased by run-time support for dynamic changes -transferring instances to a new model -and ad-hoc changes -changing the process definition for one instance. In this paper we propose a general framework for a constraint-based process modeling language and its implementation. Our approach supports both ad-hoc and dynamic change, and the transfer of instances can be done easier than in traditional approaches.

Despite the abundance of analysis techniques to discover control-flow errors in workflow designs, there is hardly any support for data-flow verification. Most techniques simply abstract from data, while data dependencies can be the source of all kinds of errors. This paper focuses on the discovery of data-flow errors in workflows. We present an analysis approach that uses so-called "anti-patterns" expressed in terms of a temporal logic. Typical errors include accessing a data element that is not yet available or updating a data element while it may be read in a parallel branch. Since the anti-patterns are expressed in terms of temporal logic, the well-known, stable, adaptable, and effective modelchecking techniques can be used to discover data-flow errors. Moreover, our approach enables a seamless integration of control flow and data-flow verification.

The problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. Stress management should start far before the stress start causing illnesses. The current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. In this paper we (1) formulate the problem of stress identification and categorization from the sensor data stream mining perspective, (2) consider a reductionist approach for arousal identification as a drift detection task, (3) highlight the major problems of dealing with GSR data, collected from a watch-style stress measurement device in normal (i.e. in non-lab) settings, and propose simple approaches how to deal with them, and (4) discuss the lessons learnt from the conducted experimental study on real GSR data collected during the recent field study.

In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all models and logs. This paper fills this gap by postulating a number of axioms for quantifying precision consistently for any log and any model. Further, we show through counter-examples that none of the existing measures consistently quantifies precision.