As databases accumulate growing amounts of data at an increasing rate, adaptive indexing becomes more and more important. At the same time, applications and their use get more agile and flexible, resulting in less steady and less predictable workload characteristics. Being inert and coarse-grained, state-of-the-art index tuning techniques become less useful in such environments. Especially the full-column indexing paradigm results in lot of indexed but never queried data and prohibitively high memory and maintenance costs. In our demonstration, we present Self-Managing Indexes, a novel, adaptive, fine-grained, autonomous indexing infrastructure. In its core, our approach builds on a novel access path that automatically collects useful index information, discards useless index information, and competes with its kind for resources to host its index information. Compared to existing technologies for adaptive indexing, we are able to dynamically grow and shrink our indexes, instead of incrementally enhancing the index granularity. In the demonstration, we visualize performance and system measures for different scenarios and allow the user to interactively change several system parameters.

Yes / In this paper, a novel multilink selection framework is developed for different applications with various quality of service (QoS) requirements in avionic systems, based on the multi-attribute decisionmaking model. Two metaheuristic algorithms are proposed to solve this model while optimizing the multilink selection performances. Multilink configuration and multi-homing capabilities are generally required for aircrafts operating in a heterogeneous wireless network environment. The first algorithm, called Analytic Hierarchy Process and Simulated Annealing (AHP-SA), utilises a two-phase process. In Phase one, an analytic hierarchy process (AHP) is used to choose the decision weight factors. Then, in Phase two, a simulated annealing process is applied to select suitable networks, for various service requests, based on the weights obtained from first phase. Further, to improve customer satisfaction, Simulated Annealing algorithm for Simultaneous Weights and Network Selection Optimisation (SA-SWNO) is developed, in which a simulated annealing algorithm is applied to dynamically optimise weight factors of objective functions and the request-to-network assignment matrix. Simulation results demonstrates that both proposed algorithms outperform the commonly used price-based or QoS-based network selection scheme with much higher averaged satisfaction degree and lower computational complexity. / Cockpit NetwOrk CoMmunications Environment Testing (COMET) Project under the European Commission’s Program Clean Sky2 in partnership with the European Aeronautical Industry

Linear time-invariant (LTI) dynamic networks are described by their dynamical structure function, and generally, they have many possible state space realizations. This work characterizes the necessary and sufficient conditions on a state transformation that preserves the dynamical structure function, thereby generating the entire set of realizations of a given order for a specific dynamic network.

Linear time-invariant (LTI) dynamic networks are described by their dynamical structure function, and generally, they have many possible state space realizations. This work characterizes the necessary and sufficient conditions on a state transformation that preserves the dynamical structure function, thereby generating the entire set of realizations of a given order for a specific dynamic network.

Linear time-invariant (LTI) dynamic networks are described by their dynamical structure function, and generally, they have many possible state space realizations. This work characterizes the necessary and sufficient conditions on a state transformation that preserves the dynamical structure function, thereby generating the entire set of realizations of a given order for a specific dynamic network.