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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables
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Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables

机译：使用分布式哈希表的地理空间时间序列数据分析查询

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摘要

As remote sensing equipment and networked observational devices continue to proliferate, their corresponding data volumes have surpassed the storage and processing capabilities of commodity computing hardware. This trend has led to the development of distributed storage frameworks that incrementally scale out by assimilating resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of research: extracting insights, relationships, and models from the underlying datasets. The focus of this study is twofold: and analytics over voluminous, multidimensional datasets in a distributed environment. Both of these types of analysis represent a higher-level abstraction over standard query semantics; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. The algorithms presented in this work were evaluated empirically on a real-world geospatial time-series dataset in a production environment, and are broadly applicable across other storage frameworks.
机译：随着遥感设备和网络化观测设备的不断扩散，它们相应的数据量已经超过了商品计算硬件的存储和处理能力。这种趋势导致了分布式存储框架的发展，该框架通过根据需要吸收资源来逐步扩展。尽管本身具有挑战性，但存储和管理大量数据集只是更广泛研究领域的先驱：从基础数据集中提取见解，关系和模型。这项研究的重点是双重的：以及在分布式环境中对大量多维数据集的分析。这两种类型的分析都代表了对标准查询语义的更高层次的抽象。我们的框架无需索引每个离散值以进行后续检索，而是自动学习数据集中维度之间的关系和相互作用，并使信息易于为用户使用。此功能包括统计概要，相关分析，假设检验，概率结构和预测模型，这些功能不仅可以发现维度之间的细微关系，而且还可以预测未来的事件和趋势。在生产环境中的真实地理空间时间序列数据集上，根据经验评估了本文中介绍的算法，该算法可广泛应用于其他存储框架。

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