Abstract: We consider the task of dynamic capability estimation for an unmanned aerial vehicle, which is needed to provide the vehicle with the ability to dynamically and autonomously sense, plan, and act in real time. Our dynamic data-driven application systems framework employs reduced models to achieve rapid evaluation runtimes. Our reduced models must also adapt to underlying dynamic system changes, such as changes due to structural damage or degradation of the system. Our dynamic reduced models take into account changes in the underlying system by directly learning from the data provided by sensors, without requiring access to the original high-fidelity model. We present here an adaptivity indicator that detects a change in the underlying system and so allows the initiation of the dynamic reduced modeling adaptation if necessary. The adaptivity indicator monitors the error of the dynamic reduced model by comparing model predictions with sensor data, and signals a change if the error exceeds a given threshold. The indicator is demonstrated on a deflection model of a damaged plate in bending. Local damage of the plate is modeled by a change in the thickness of the plate. The numerical results show that in this example the adaptivity indicator detects all changes in the thickness and correctly initiates the adaptation of the reduced model.

Abstract: In this paper, we introduce new methods for multiobjective, system-level optimization that have been incorporated into the Lightweight Dataflow for Dynamic Data Driven Application Systems (DDDAS) Environment (LiD4E). LiD4E is a design tool for optimized implementation of dynamic, data-driven stream mining systems using high-level dataflow models of computation. More specifically, we develop in this paper new methods for integrated modeling and optimization of real-time stream mining constraints, multidimensional stream mining performance (precision and recall), and energy efficiency. Using a design methodology centered on data-driven control of and coordination between alternative dataflow subsystems for stream mining (classification modes), we develop systematic methods for exploring complex, multidimensional design spaces associated with dynamic stream mining systems, and deriving sets of Pareto-optimal system configurations that can be switched among based on data characteristics and operating constraints.

Abstract: The DDDAS paradigm, unifying applications, mathematical modeling, and sensors, is now more relevant than ever with the advent of Large-Scale/Big-Data and Big-Computing. Large-Scale-Dynamic-Data (advertised as the next wave of Big Data) includes the integrated range of data from high-end systems and instruments together with the dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems. In this paper we present Fresh Breeze, a dataflow-based execution and programming model and computer architecture and how it provides a sound basis to develop future computing systems that match the DDDAS challenges. The DDDAS' computation patterns and data storage needs are well matched by the Fresh Breeze system's codelet-based execution model and memory-chunk-based memory model, as well as the proposed ISA level architecture features to be highlighted in this paper. We have extended and improved a previous generation of Fresh Breeze simulation platform to model a Fresh Breeze processing chip comprising up to 64 processing cores with an ISA with new features to address the issues of efficient symbiotic processing, and have completed a compiler tool chain from an adapted version of the Java source language to machine-level codelets for the simulator. We have evaluated our current implementation on several standard kernels from linear algebra for which near-linear speedup versus the number of cores is achieved without manual parallelization or scale-specific performance tuning. These test kernels show effectiveness of the fine-grain task scheduling and load balancing features essential to achieving the best performance for DDDAS. It is expected that once planned support of stream computation and transaction processing is checked out, it will be possible to demonstrate superior performance for application codes of DDDAS.

Abstract: The deployment of networks of sensors and development of pertinent information processing techniques can facilitate the requirement of situational awareness present in many defense/surveillance systems. Sensors allow the collection and distributed processing of information in a variety of environments whose structure is not known and is dynamically changing with time. A distributed dynamic data driven (DDDAS-based) framework is developed in this paper to address distributed multi-threat tracking under limited sensor resources. The acquired sensor data will be used to control the sensing part of the sensor network, and utilize only the sensing devices that acquire good quality measurements about the present targets. The DDDAS-based concept will be utilized to enable efficient sensor activation of only those parts of the network located close to a target/object. A novel combination of stochastic filtering techniques, drift homotopy and sparsity-inducing canonical correlation analysis (S-CCA) is utilized to dynamically identify the target-informative sensors and utilize them to perform improved drift-based particle filtering techniques that will allow robust, stable and accurate distributed tracking of multiple objects. Numerical tests demonstrate the effectiveness of the novel framework.

Abstract: In software development, migration from a Data Base Management System (DBMS) to another, especially with distinct characteristics, is a challenge for programmers and database administrators. Changes in the application code in order to comply with new DBMS are usually vast, causing migrations infeasible. In order to tackle this problem, we present NoSQLayer, a framework capable to support conveniently migrating from relational (i.e., MySQL) to NoSQL DBMS (i.e., MongoDB). This framework is presented in two parts: (1) migration module; and, (2) mapping module. The first one is a set of methods enabling seamless migration between DBMSs (i.e. MySQL to MongoDB). The latter provides a persistence layer to process database requests, being capable to translate and execute these requests in any DBMS, returning the data in a suitable format as well. Experiments show NoSQLayer as a handful solution suitable to handle large volume of data (e.g., Web scale) in which traditional relational DBMS might be inept in the duty.