分布式数据集中主动数据分配的包围方案
An Ensemble Scheme for Proactive Data Allocation in Distributed Datasets
摘要：
物联网（IoT）的出现为众多设备提供了与其环境交互、收集和处理数据的机会。数据以向上模式通过边缘计算（EC）基础设施传输到云。大量的EC节点成为分布式数据集的主机，在这些数据集中，可以与最终用户近距离地实现各种处理活动。这种方法可以限制提供响应的延迟。在本文中，我们专注于一个模型，该模型主动决定收集的数据应该存储在哪里，以最大限度地提高EC基础设施中数据集的准确性。我们认为准确性是由数据集的可靠性定义的，数据集的可靠性表现为数据的统计相似性。我们讨论了传入数据与可用数据集的相似性，并选择最合适的数据集来存储新信息。为了减轻处理节点的负担，一个连续的，复杂的统计处理，我们建议使用的概要作为主题的相似性过程。传入的数据与基于集成方案的可用概要进行匹配，然后，我们选择合适的主机来存储它们并执行相应概要的更新。我们提供了问题的描述和我们的解决方案的制定。我们的实验评估目标，以揭示所提出的方法的性能。
英文摘要：
The advent of the Internet of Things (IoT) gives the opportunity to numerous devices to interact with their environment, collect and process data. Data are transferred, in an upwards mode, to the Cloud through the Edge Computing (EC) infrastructure. A high number of EC nodes become the hosts of distributed datasets where various processing activities can be realized in close distance with end users. This approach can limit the latency in the provision of responses. In this paper, we focus on a model that proactively decides where the collected data should be stored in order to maximize the accuracy of datasets present at the EC infrastructure. We consider that the accuracy is defined by the solidity of datasets exposed as the statistical resemblance of data. We argue upon the similarity of the incoming data with the available datasets and select the most appropriate of them to store the new information. For alleviating processing nodes from the burden of a continuous, complicated statistical processing, we propose the use of synopses as the subject of the similarity process. The incoming data are matched against the available synopses based on an ensemble scheme, then, we select the appropriate host to store them and perform the update of the corresponding synopsis. We provide the description of the problem and the formulation of our solution. Our experimental evaluation targets to reveal the performance of the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
