Cheetah Experimental Platform (CEP)

As discussed in the research statement, our team focuses on empirical research methods. Over the years, by continuously conducting experiments and reflecting problematic situations, it became apparent that tool support is indispensable for efficiently conducting empirical research. In particular, data collection during experiment should be reliable and automated. The collected data, in turn, should be amenable to a broad range of data analysis. And, for collected data, it is absolutely essential that it was produced by subjects that obeyed the experiment’s setup. Last but not least, it should be possible to employ the same component, such as a BPMN modeling editor, for series of experiments in order to make data comparable. To address these problems, we have developed Cheetah Experimental Platform (CEP)—a detailed description of CEP’s capabilities as well as a demo version can be found here.

Cheetah Experimental Platform Web (CEP-Web)

In the past, we have been using CEP in order to investigate, among other research objectives, the creation of process models, i.e., the Process of Process Modeling (PPM). Utilizing CEP’s detailed logging mechanism proved valuable to gain deep insights into how process models are created. What largely remained in the realm of darkness, were the cognitive processes involved in the creation of process models. Therefore, we started to work with eye movement analysis in order to gain a more complete understanding of the cognitive processes during the PPM. In this context, we came across an interesting approach for evaluating the cognitive load or mental effort of modelers. Literature reports that cognitive load can be assessed by measuring the diameter of a human’s pupil. Essentially, an increase in pupil size corresponds to an increase in mental effort. While the backgrounds are relatively well understood, the state of the art is less evolved when it comes to efficiently supporting such studies in terms of data processing, data cleaning, and data analysis (modern eye trackers collect up to a 1000 data points per second – good tool support is certainly necessary!). Therefore, we decided to develop a new tool to efficiently be able to perform pupillometric cognitive load analysis. All information on CEP-Web can be found here.

Alaska Simulator

Another tool developed by our team is Alaska Simulator, whose focus is twofold. First, Alaska Simulator offers operational support for different levels of process flexibility, such as provided by Late Binding, Late Modeling or Late Composition. By supporting these techniques through a uniform user interface, Alaska Simulator enables the empirical comparison of aforementioned approaches. Second, Alaska Simulator was designed to teach decision deferral techniques by providing various scenarios that illustrate benefits and drawbacks. Thereby, Alaska Simulator adopts the metaphor of a journey for illustration. Detailed information about Alaska Simulator as well as a demo version can be found here.

Test Driven Modeling Suite

Test Driven Modeling Suite (TDMS) has been implemented in order to provide operational support for the concepts of Test Driven Modeling (TDM). More information about TDM in general and how to obtain a free copy of TDMS can be found here.