Cheetah Experimental Platform Web 1.0: Cleaning Pupillary Data

Recently, researchers started using cognitive load in various settings, e.g., educational psychology, cognitive load theory, or human-computer interaction. Cognitive load characterizes a tasks' demand on the limited information processing capacity of the brain. The widespread adoption of eye-tracking devices led to increased attention for objectively measuring cognitive load via pupil dilation. However, this approach requires a standardized data processing routine to reliably measure cognitive load. This technical report presents CEP-Web, an open source platform to providing state of the art data processing routines for cleaning pupillary data combined with a graphical user interface, enabling the management of studies and subjects. Future developments will include the support for analyzing the cleaned data as well as support for Task-Evoked Pupillary Response (TEPR) studies

Investigating expressiveness and understandability of hierarchy in declarative business process models

Hierarchy has widely been recognized as a viable approach to deal with the complexity of conceptual models. For instance, in declarative business process models, hierarchy is realized by sub-processes. While technical implementations of declarative sub-processes exist, their application, semantics, and the resulting impact on understandability are less understood yet—this research gap is addressed in this work. More specifically, we discuss the semantics and the application of hierarchy and show how subprocesses enhance the expressiveness of declarative modeling languages. Then, we turn to the influence of hierarchy on the understandability of declarative process models. In particular, we present a cognitive-psychology-based framework that allows to assess the impact of hierarchy on the understandability of a declarative process model. To empirically test the proposed framework, a combination of quantitative and qualitative research methods is followed. While statistical tests provide numerical evidence, think-aloud protocols give insights into the reasoning processes taking place when reading declarative process models

Understandability Issues of Approaches Supporting Business Process Variability

The increasing adoption of Process-Aware Information Systems, together with the reuse of process knowledge, has led to the emergence of process model repositories with large process families, i.e., collections of related process model variants. For managing such related model collections two types of approaches exist. While behavioral approaches take supersets of variants and derive a process variant by hiding and blocking process elements, structural approaches take a base process model as input and derive a process variant by applying a set of change operations to it. However, at the current stage no framework for assessing these approaches exists and it is not yet clear which approach should be better used and under which circumstances. Therefore, to give first insights about this issue, this work compares both approaches in terms of understandability of the produced process model artifacts, which is fundamental for the management of process families and the reuse of their contained process fragments. In addition, the comparison can serve as theoretical basis for conducting experiments as well as for fostering the development of tools managing business process variability

Making Sense of Declarative Process Models: Common Strategies and Typical Pitfalls

Declarative approaches to process modeling are regarded as well suited for highly volatile environments as they provide a high degree of flexibility. However, problems in understanding and maintaining declarative business process models impede often their usage. In particular, how declarative models are understood has not been investigated yet. This paper takes a first step toward addressing this question and reports on an exploratory study investigating how analysts make sense of declarative process models. We have handed out real-world declarative process models to subjects and asked them to describe the illustrated process. Our qualitative analysis shows that subjects tried to describe the processes in a sequential way although the models represent circumstantial information, namely, conditions that produce an outcome, rather than a sequence of activities. Finally, we observed difficulties with single building blocks and combinations of relations between activities