This paper describes a United States Air Force Advanced Technology Demonstration (ATD) that applied value-based scheduling to produce an adaptive, distributed tracking component appropriate for consideration by the Airborne Warning and Control System (AWACS) program. This tracker was designed to evaluate application-specific Quality of Service (QoS) metrics to quantify its tracking services in a dynamic environment and to derive scheduling parameters directly from these QoS metrics to control tracker behavior. The prototype tracker was implemented on the MK7 operating system, which provided native value-based processor scheduling and a distributed thread programming abstraction. The prototype updates all of the tracked-object records when the system is not overloaded, and gracefully degrades when it is. The prototype has performed extremely well during demonstrations to AWACS operators and tracking system designers. Quantitative results are presented.
MITRE's Evolvable Real-Time C3 (Command, Control, and Communications) project has developed an approach that would enable current real-time systems to evolve into the systems of the future. This paper first summarizes the design and implementation of an infrastructure for an evolvable real-time C3 system. Then, a detailed discussion of the infrastructure requirements for a survivable real-time C3 system is presented. Finally security issues for survivability, as well as open implementation of the infrastructure, are described. In particular, adaptable middleware for survivable systems is discussed.
In this paper we discuss the need for real-time data mining for many applications in government and industry and describe resulting research issues. We also discuss dependability issues including incorporating security, integrity, timeliness and fault tolerance into data mining. Several different data mining outcomes are described with regard to their implementation in a real-time environment. These outcomes include clustering, association-rule mining, link analysis and anomaly detection. The paper describes how they would be used together in various parallel-processing architectures. Stream mining is discussed with respect to the challenges of performing data mining on stream data from sensors. The paper concludes with a summary and discussion of directions in this emerging area.
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