Expert Distributed Knowledge Systems for Aerospace Applications

Many aerospace applications produce vast amounts of data that must be analyzed intelligently as rapidly as possible, often with near- real-time constraints. Examples include telemetry data from a variety of sources, scientific data from space missions, and design and performance data for complex devices. Extracting knowledge from such data is a demanding task requiring powerful computer hardware and appropriate software architectures. On the hardware side, there is growing interest in parallel computers, and especially in parallel "hypercomputers" that may be created from clusters of powerful RISC workstations, now available from many vendors and installed widely within NASA and the aerospace industry. This project addresses the software required for knowledge processing on distributed or parallel computers. Specifically, we will develop novel distributed knowledge processing software for workstation clusters and shared- or distributed-memory parallel computers embodying both hierarchical real-time data fusion and expert database capabilities. Such unified "expert data fusion" software should be uniquely capable of supporting both rapid analysis of evolving data streams and smart knowledge extraction from legacy data. By building the software so that it leverages existing tools, techniques, and databases, we will be able to deliver tremendous productivity increases to users in the aerospace community.