Spatial alarms are considered as one of the basic capabilities in future mobile computing systems for enabling personalization of location-based services. In this paper, we propose a distributed architecture and a sui...
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(纸本)9781424444816
Spatial alarms are considered as one of the basic capabilities in future mobile computing systems for enabling personalization of location-based services. In this paper, we propose a distributed architecture and a suite of safe region techniques for scalable processing of spatial alarms. We show that safe region-based processing enables resource optimal distribution of partial alarm processing tasks from the server to the mobile clients. We propose three different safe region computation algorithms to explore the impact of size and shape of the safe region on network bandwidth, server load and client energy consumption. Concretely, we show that the maximum weighted perimeter rectangular safe region approach outperforms previous techniques in terms of performance and accuracy. We further explore finer granularity safe regions by introducing grid-based and pyramid-based representation of rectilinear polygonal shapes using bitmap encoding. Our experimental evaluation shows that the distributed safe region-based architecture outperforms the two most popular server-centric approaches, periodic and safe period-based, for spatial alarm processing.
The article provides an overview of recent work on distributed adaptive algorithms. The work focuses mainly on incremental and diffusion strategies and comments on the mean-square-error performance of the incremental ...
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The article provides an overview of recent work on distributed adaptive algorithms. The work focuses mainly on incremental and diffusion strategies and comments on the mean-square-error performance of the incremental solution.
DPTS (distributed processing test simulator) is a protocol and associated system-level software that result in a system in which the software can efficiently interact across processor boundaries, provide some measure ...
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DPTS (distributed processing test simulator) is a protocol and associated system-level software that result in a system in which the software can efficiently interact across processor boundaries, provide some measure of processor and/or communications fault tolerance, enhance cost effectiveness and reduce life-cycle cost, permit decreased dependency on system complexity, and eliminate concern over certain aspects of system design. Additionally, the protocol enhances the effectiveness of systems requiring rapid reconfigurability, such as simulators. The environment being targeted by the effort is described. The overall design concepts are described, followed by a discussion of the steps necessary to achieve the targeted design. Finally, the current state of the effort is presented.< >
The paper addresses the problem of efficiently querying large numbers of text documents using parallel processing methods. The optimization criteria are somewhat different from those used in querying heterogeneous dat...
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The paper addresses the problem of efficiently querying large numbers of text documents using parallel processing methods. The optimization criteria are somewhat different from those used in querying heterogeneous databases, largely because the extraction of ontological information from documents is the dominant component of query execution time. We assume that each document has been previously annotated using XML. The authors describe the architecture of a system to process ontology based queries for XML annotated documents. We have introduced two basic strategies for query processing: simple strategy, and semi-join strategy, and their possible extensions using pipelining and longer lists for keyword search. Different levels of parallelism for these strategies are discussed. An evaluation model is created and used to derive optimal replication of resource agents. The theoretical and experimental results are compared.
NASA Technical Reports Server (Ntrs) 19890017102: Real-Time Hierarchically distributed processing Network Interaction Simulation by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19890017102: Real-Time Hierarchically distributed processing Network Interaction Simulation by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19880007011: Application of Parallel distributed processing to Space Based Systems by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19880007011: Application of Parallel distributed processing to Space Based Systems by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 20030015489: Bi-Level Integrated System Synthesis (Bliss) for Concurrent and distributed processing by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 20030015489: Bi-Level Integrated System Synthesis (Bliss) for Concurrent and distributed processing by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19900020536: Rcts: a Flexible Environment for Sensor Integration and Control of Robot Systems; the distributed processing Approach by NASA Technical Reports Server (Ntrs); NASA Tec...
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NASA Technical Reports Server (Ntrs) 19900020536: Rcts: a Flexible Environment for Sensor Integration and Control of Robot Systems; the distributed processing Approach by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by
Entity Resolution (ER) can be characterized as the process by which various records (references) become linked to the entities (identities) with which they related. The ER process becomes exponentially more complex an...
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Entity Resolution (ER) can be characterized as the process by which various records (references) become linked to the entities (identities) with which they related. The ER process becomes exponentially more complex and time consuming as datasets approach “Big Data” volumes. Datasets that are beyond the ER capabilities of even the most powerful computers are no longer unusual. The overarching objective is to significantly reduce the amount of elapsed time required to achieve all aspects of the ER life cycle when contrasted with conventional implementations and algorithms. Reductions approaching an order of magnitude are conceivable. Reductions in Big Data ER from weeks to days, days to hours, and similar can be attained given ER life cycle and ER via parallel deployments.
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