Computational framework for interactive architecting of complex systems

Presented is a novel framework for interactive systems architecture definition at early design stages. It incorporates graph‐theoretic data structures, entity relationships, and algorithms that enable the systems architect to operate interactively and simultaneously in different domains. It explicitly captures the “zigzagging” of the functional reasoning process, including not only allocated, but also the derived functions. A prototype software tool, AirCADia Architect, was implemented, which allowed the framework to be demonstrated to and tried hands‐on by practicing aircraft systems architects. The tool enables architects to effectively express their ideas when interactively synthesizing new architectures, while still retaining control over the process. The proposed approach was especially acknowledged as the way forward for rationale capture

Application of Executable Architecture in Early Concept Evaluation using the DoD Architecture Framework

The increasing complexity in the development of today\u27s modern warfighting systems demands a systematic evaluation approach in the assessment of the envisaged capability and estimating the cost effectiveness, especially in the early stages of Concept Development. This research focused on the development of early Concept evaluation methodology through the use of Executable Architecture (EA) through the System Architecting process. Particularly, the methodology was applied in the assessment of a proposed Multi-tiered Unmanned Aircraft System System-of-System that is designed provide target acquisition and conduct dynamic strike on Theater Ballistic Missile launchers. Through the implementation of the evaluation methodology using dynamic modeling of the system-under-design, the research was able to provide quantitative assessment of different design parameters on the overall system effectiveness, as measured using a set of pre-determined Measures-of-Effectiveness. Specifically, Innoslate was used to develop the EA model of a fictitious multi-tier Unmanned Aircraft System System-of-Systems, and provided quantitative assessment of the overall system performance due to changes in the design parameters. Specification, the research showed that the proposed evaluation methodology provides system architects with the tool to 1) evaluate different design parameters, 2) understand the overall system capability given sub-system capabilities, and 3) determine sub-system requirement given desired system performance

Framework of Big Data Analytics in Real Time for Healthcare Enterprise Performance Measurements

Healthcare organizations (HCOs) currently have many information records about their patients. Yet, they cannot make proper, faster, and more thoughtful conclusions in many cases with their information. Much of the information is structured data such as medical records, historical data, and non-clinical information. This data is stored in a central repository called the Data Warehouse (DW). DW provides querying and reporting to different groups within the healthcare organization to support their future strategic initiatives. The generated reports create metrics to measure the organization\u27s performance for post-action plans, not for real-time decisions. Additionally, healthcare organizations seek to benefit from the semi-structured and unstructured data by adopting emerging technology such as big data to aggregate all collected data from different sources obtained from Electronic Medical Record (EMR), scheduling, registration, billing systems, and wearable devices into one volume for better data analytic. For data completeness, big data is an essential element to improve healthcare systems. It is expected to revamp the outlook of the healthcare industry by reducing costs and improving quality. In this research, a framework is developed to utilize big data that interconnects all aspects of healthcare for real-time analytics and performance measurements. It is a comprehensive framework that integrates 41 integrated components in 6 layers: Organization, People, Process, Data, Technology, and Outcomes to ensure successful implementation. Each component in the framework and its linkage with other components are explained to show the coherency. Moreover, the research highlights how data completeness leads to better healthcare quality outcomes, and it is essential for healthcare organization survival. Additionally, the framework offers guidelines for selecting the appropriate technology with the flexibility of implementing the solution on a small or large scale, considering the benefits vs. investment. A case study has been used to validate the framework, and interviews with Subject Matter Experts (SMEs) have been conducted to provide another valuable perspective for a complete picture. The findings revealed that focusing only on big data technology could cause failing implementation without accomplishing the desired value of the data analytics outcomes. It is only applied for one-dimensional, not at the enterprise level. In addition, the framework proposes another 40 components that need to be considered for a successful implementation. Healthcare organizations can design the future of healthcare utilizing big data and analytics toward the fourth revolution in healthcare known as Healthcare 4.0 (H 4.0). This research is a contribution to this effort and a response to the needs