Abstract: Mitigating human errors is a priority in the design of complex systems, especially through the use of body area networks. This paper describes early developments of a dynamic data driven platform to predict operator error and trigger appropriate intervention before the error happens. Using a two-stage process, data was collected using several sensors (e.g. electroencephalography, pupil dilation measures, and skin conductance) during an established protocol - the Stroop test. The experimental design began with a relaxation period, 40 questions (congruent, then incongruent) without a timer, a rest period followed by another two rounds of questions, but under increased time pressure. Measures such as workload and engagement showed responses consistent with what is known for Stroop tests. Dynamic system analysis methods were then used to analyze the raw data through principal components analysis and least squares complex exponential method. The results show that this algorithm has the potential to capture mental states in a mathematical fashion, thus enabling the possibility of prediction.

Abstract: Dynamic execution of a business process requires the selection and composition of multiple existing services regardless of their locations, platforms, execution speeds, etc. Thus web service selection appears as a challenging and elusive task especially when the service task has to be executed based on user requirements at the runtime. This paper presents our Semantic-Based Business Process Execution Engine (SBPEE) for the dynamic execution of business processes by the orchestration of various exposed web services. SBPEE is based on our designed Project Domain Ontology (PrjOnt) that captures user specifications and SWRL rules which classify the user specification into a specific category according to the business logic and requirements of an enterprise. Based on this classification of the user project and requirements, our semantic engine selects web services from the service repository for the dynamic execution of a business process. SBPEE matches functional requirements of a web service and required QoS attributes to identify the list of pertinent candidate services to fulfil the complex business process transactions. Finally, we present our case study on Create Order business process that aims at creating an order for the customer by following various web services for its task completion.

Abstract: Dynamic Data-Driven Avionics Systems (DDDAS) embody ideas from the Dynamic Data-Driven Application Systems paradigm by creating a data-driven feedback loop that analyzes spatio-temporal data streams coming from aircraft sensors and instruments, looks for errors in the data signaling potential failure modes, and corrects for erroneous data when possible.In case of emergency, DDDAS need to provide enough information about the failure to pilots to support their decision making in real-time. We have developed the PILOTS system, which supports data-error tolerant spatio-temporal stream processing, as an initial step to realize the concept of DDDAS. In this paper, we apply the PILOTS system to actual data from the Tuninter 1153 (TU1153) ight accident in August 2005, where the installation of an incorrect fuel sensor led to a fatal accident. The underweight condition suggesting an incorrect fuel indication for TU1153 is successfully detected with 100% accuracy during cruise ight phases. Adding logical redundancy to avionics through a dynamic data-driven approach can significantly improve the safety of flight.

Abstract: The OpenDBDDAS Toolkit is a software framework to provide support for more easily creating and expanding dynamic big data-driven application systems (DBDDAS) that are common in environmental systems, many engineering applications, disaster management, traffic management, and manufacturing. In this paper, we describe key features needed to implement a secure MapReduce and Hadoop-like system for high performance clusters that guarantees a certain level of privacy of data from other concurrent users of the system. We also provide examples of a secure MapReduce prototype and compare it to another high performance MapReduce, MR-MPI.