Data Provenance

Wir werden eine grafische Benutzeroberfläche für das Forschungsprojekt ProSA entwickeln. Das Ziel von ProSA ist die Bestimmung der für ein Anfrageergebnis notwendigen, minimalen Quelldaten im Rahmen von Provenance Management. ProSA nutzt das Java-Tool ChaTEAU, um aus der Quell-Datenbank und einer SQL-Anfrage die minimale Teildatenbank zu bestimmen. ChaTEAU bekommt eine s-t tgd in Form eines XML-Dokuments und auf dessen Basis wird mit seiner Implementierung der Chase-Algorithmus zwei Mal ausgeführt. Nach der ersten Ausführung hat man das Anfrageergebnis beziehungsweise die Ergebnisinstanz. Beim zweiten Ausführen von ChaTEAU wird als Eingabe die Inverse Anfrage genommen und als Ergebnis erhält man dann die minimale Teildatenbank. Außerdem werden mithilfe des Provenance-Typs und der SQL-Anfrage Provenance-Berechnungen durchgeführt, dessen Ergebnisse dann mit der minimalen Teildatenbank kombiniert werden. Diese wird dann in der von uns entwickelten Benutzeroberfläche von ProSA ausgegeben

Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns

Simulation experiments are typically conducted repeatedly during the model development process, for example, to re-validate if a behavioral property still holds after several model changes.Approaches for automatically reusing and generating simulation experiments can support modelers in conducting simulation studies in a more systematic and effective manner.They rely on explicit experiment specifications and, so far, on user interaction for initiating the reuse. Thereby, they are constrained to support the reuse of simulation experiments in a specific setting. Our approach now goes one step further by automatically identifying and adapting the experiments to be reused for a variety of scenarios.To achieve this, we exploit provenance graphs of simulation studies, which provide valuable information about the previous modeling and experimenting activities, and contain meta-information about the different entities that were used or produced during the simulation study. We define provenance patterns and associate them with a semantics, which allows us to interpret the different activities, and construct transformation rules for provenance graphs.Our approach is implemented in a Reuse and Adapt framework for Simulation Experiments (RASE) which can interface with various modeling and simulation tools. In the case studies, we demonstrate the utility of our framework for a) the repeated sensitivity analysis of an agent-based model of migration routes, and b) the cross-validation of two models of a cell signaling pathway

Early Results of Experiments with Responsive Open Learning Environments

Abstract: Responsive open learning environments (ROLEs) are the next generation of personal learning environments (PLEs). While PLEs rely on the simple aggregation of existing content and services mainly using Web 2.0 technologies, ROLEs are transforming lifelong learning by introducing a new infrastructure on a global scale while dealing with existing learning management systems, institutions, and technologies. The requirements engineering process in highly populated test-beds is as important as the technology development. In this paper, we will describe first experiences deploying ROLEs at two higher learning institutions in very different cultural settings. The Shanghai Jiao Tong University in China and at the “Center for Learning and Knowledge Management and Department of Information Management in Mechanical Engineering ” (ZLW/IMA) at RWTH Aachen University have exposed ROLEs to theirs students in already established courses. The results demonstrated to readiness of the technology for large-scale trials and the benefits for the students leading to new insights in the design of ROLEs also for more informal learning situations