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The query and search policy of supporting complex query over multi-dimensional data is researched on the unstructured P2P network with super-peer, and a comprehensive framework is proposed to apply for sharing, indexing and querying multi-dimensional dataset. Based on the indexing tree of R*-tree, a new adaptive indexing tree named Extended Indexing R*-tree (EIR-tree) is proposed. All the methods such as the information collection and maintenance and the EIR-tree construction and update are discussed.  相似文献
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Real-time embedded systems are often specified as a collection of independent tasks, each generating a sequence of event-triggered code blocks. The goal of scheduling tasks in this domain is to find an execution order which satisfies all real-time constraints. Within the context of recurring real-time tasks, all previous work either allowed preemptions, or only considered dynamic scheduling, and generally had exponential complexity. However, for many embedded systems running on limited resources, preemptive scheduling may be very costly due to high context switching and memory overheads, and dynamic scheduling can be less desirable due to high CPU overhead. In this paper, we study static priority scheduling of recurring real-time tasks. We focus on and obtain schedule-theoretic results for the non-preemptive uniprocessor case. To achieve this, we derive a sufficient (albeit not necessary) condition for schedulability under static priority scheduling and show that this condition can be efficiently tested in practice. The latter technique is demonstrated with examples, where in each case, an optimal solution for a given problem specification is obtained within reasonable time, by first detecting good candidates using meta-heuristics, and then by testing them for schedulability.
Selin Cerav-ErbasEmail:
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在具有超级结点的非结构化P2P系统中,研究了复杂多维数据的查询搜索策略,提出了一个应用于具有超级结点的非结构化P2P网络的综合框架,在该框架中,能够实现对多维数据共享、索引以及查询等操作的处理。以R^*-tree索引树为基础,提出了一种能够应用于P2P的扩展R^*-tree索引树,即EIR-tree树,研究了系统中集群信息的收集与维护、EIR-tree树的构建与维护等方法和措施。  相似文献
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Creative group work can be supported by collaborative search and annotation of Web resources. In this setting, it is important to help individuals both stay fluent in generating ideas of what to search next (i.e., maintain ideational fluency) and stay consistent in annotating resources (i.e., maintain organization). Based on a model of human memory, we hypothesize that sharing search results with other users, such as through bookmarks and social tags, prompts search processes in memory, which increase ideational fluency, but decrease the consistency of annotations, e.g., the reuse of tags for topically similar resources. To balance this tradeoff, we suggest the tag recommender SoMe, which is designed to simulate search of memory from user-specific tag-topic associations. An experimental field study (= 18) in a workplace context finds evidence of the expected tradeoff and an advantage of SoMe over a conventional recommender in the collaborative setting. We conclude that sharing search results supports group creativity by increasing the ideational fluency, and that SoMe helps balancing the evidenced fluency-consistency tradeoff.  相似文献
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This paper describes the Java Metaheuristics Search framework (JAMES, v1.1): an object‐oriented Java framework for discrete optimization using local search algorithms that exploits the generality of such metaheuristics by clearly separating search implementation and application from problem specification. A wide range of generic local searches are provided, including (stochastic) hill climbing, tabu search, variable neighbourhood search and parallel tempering. These can be applied to any user‐defined problem by plugging in a custom neighbourhood for the corresponding solution type. Using an automated analysis workflow, the performance of different search algorithms can be compared in order to select an appropriate optimization strategy. Implementations of specific components are included for subset selection, such as a predefined solution type, generic problem definition and several subset neighbourhoods used to modify the set of selected items. Additional components for other types of problems (e.g. permutation problems) are provided through an extensions module which also includes the analysis workflow. In comparison with existing Java metaheuristics frameworks that mainly focus on population‐based algorithms, JAMES has a much lower memory footprint and promotes efficient application of local searches by taking full advantage of move‐based evaluation. Releases of JAMES are deployed to the Maven Central Repository so that the framework can easily be included as a dependency in other Java applications. The project is fully open source and hosted on GitHub. More information can be found at http://www.jamesframework.org . Copyright © 2016 John Wiley & Sons, Ltd.  相似文献
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