Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe

In order to build better human-friendly human-computer interfaces, such interfaces need to be enabled with capabilities to perceive the user, his location, identity, activities and in particular his interaction with others and the machine. Only with these perception capabilities can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the development of novel techniques for the visual perception of humans and their activities, in order to facilitate perceptive multimodal interfaces, humanoid robots and smart environments. My work includes research on person tracking, person identication, recognition of pointing gestures, estimation of head orientation and focus of attention, as well as audio-visual scene and activity analysis. Application areas are humanfriendly humanoid robots, smart environments, content-based image and video analysis, as well as safety- and security-related applications. This article gives a brief overview of my ongoing research activities in these areas

On the Use of Queueing Petri Nets for Modeling and Performance Analysis of Distributed Systems

Predictive performance models are used increasingly throughout the phases of the software engineering lifecycle of distributed systems. However, as systems grow in size and complex-ity, building models that accurately capture the different aspects of their behavior becomes a more and more challenging task. The challenge stems from the limited model expressivenes

Automated extraction of palladio component models from running enterprise Java applications

Nowadays, software systems have to fulfill increasingly strin-gent requirements for performance and scalability. To ensure that a system meets its performance requirements during op-eration, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of perfor-mance models of Java EE applications, based on monitor-ing data collected during operation. We extract instances of the Palladio Component Model (PCM)- a performance meta-model targeted at component-based systems. We eval-uate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed. 1

Automated extraction of architecture-level performance models of distributed component-based systems

Abstract—Modern enterprise applications have to satisfy in-creasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on mon-itoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent. I