Data-Adaptable Modeling and Optimization for Runtime Adaptable Systems
Abstract
Dynamic data driven application systems (DDDAS) involve complex sensing and decision-making algorithms that operate on vast data streams with dynamic characteristics. As the availability and quality of the sensed data changes, the underlying models and decision algorithms should continually adapt in order to meet desired high-level requirements. Due to the complexity of such dynamic data-driven systems, traditional design time techniques are incapable of producing a solution that remains optimal in the face of dynamically changing data, algorithms, and even availability of computational resources. Additionally, modern approaches to DDDAS design the adaptation laws for dynamic behavior as part of the system itself, thereby resulting in a point solution for that specific application. This research project developed generalized approaches to DDDAS so that the benefits of adaptability can be extended to other applications, without resorting to application-specific point solutions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 08, 2016
- Accession Number
- AD1011170
Entities
People
- Jonathan Sprinkle
- Roman Lysecky
Organizations
- University of Arizona