An Aeronautical Pub/Sub System with Imperfect Spatiotemporal Filters

The permanent growth of air traffic volume is reaching the limits of what current Air Traffic Management (ATM) systems are capable to handle. Among the major impediments to further growth are the bottlenecks of the current heterogeneous data models and distribution systems. For the future ATM system envisioned in SESAR and NextGen, the large R&D programmes underway in Europe and the USA, the approach to System-Wide Information Management (SWIM) focuses on these issues. This thesis presents two fundamental elements of SWIM: concepts for a distributed publish/subscribe system for the dissemination of (aeronautical) events, and a generic, yet aviation-specific, model of space and time, on which the aeronautical event model and the spatiotemporal pub/sub subscription model are based. Furthermore, algorithms are presented that enhance the scalability of the overall system by merging and simplifying client subscription filters, thus reducing filter handling overhead. The approach is based on heuristically estimating the quality of merger filters, for which different proposals are made and evaluated in extensive experiments.