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Review of Ubiquitous Computing Based on Markov Chain and High Complexity Data Streaming Model

机译:基于马尔可夫链和高复杂度数据流模型的泛在计算研究述评

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Review of tourism informationization based on the Markov Chain and the high complexity data streaming model is studied in this manuscript. Most of the recommendation systems are poor in scalability due to their own defects. Although some optimization strategies have been adopted to improve them, most of them are further optimized and mainly run on a single server. Hence, this paper has some modifications. First, the choice of cache node location can be attributed to the resource allocation problem, which is also focus of research in doud environment, we construct the efficient data center for preparing. Second, the grid resource discovery method must be able to adapt to the grid environment circle with characteristics of some large scale, heterogeneity, distribution, dynamic, openness and so on. We find the optimal model to avoid the time delay. The experiment results compared with the latest methods have been presented.
机译:本文研究了基于马尔可夫链和高复杂度数据流模型的旅游信息化研究。大多数推荐系统由于其自身的缺陷而在可伸缩性方面较差。尽管已采用了一些优化策略来对其进行改进,但是大多数优化策略仍在进一步优化中,并且主要在单个服务器上运行。因此,本文有一些修改。首先,缓存节点位置的选择可以归因于资源分配问题,这也是在虚拟环境中研究的重点,我们构建了高效的数据中心进行准备。其次,网格资源发现方法必须能够适应具有较大规模,异构性,分布性,动态性,开放性等特点的网格环境圈。我们找到了避免时间延迟的最佳模型。提出了与最新方法进行比较的实验结果。

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