dragon: Multidimensional range queries on distributed aggregation trees
dragon: Multidimensional range queries on distributed aggregation trees
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DOI：
10.1016/j.future.2015.07.020
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
E. Carlini;Alessandro Lulli;L. Ricci
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文献类型：
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作者：
E. Carlini;Alessandro Lulli;L. Ricci
分布式查询处理在物联网 (IoT) 和网络物理系统等下一代分布式服务中至关重要。即使已经为点对点系统提出了几种多属性范围查询支持，也必须重新考虑这些解决方案，以完全满足物联网新计算范式（如雾计算）的要求。本文提出了dragon，一种对分布式多维范围查询处理的有效支持，旨在对高度动态的数据进行高效的查询解析。位于网络边缘的Indragon节点收集并发布多维数据。这些节点共同管理存储数据摘要的聚合树，然后在解析查询时利用该聚合树来修剪包含很少或不包含相关匹配的子树。通过空间填充曲线线性化属性空间来管理多属性查询。我们在广泛的实验设置中广泛分析了不同的聚合和查询解析策略。我们证明龙可以有效地管理快速变化的数据值。此外，我们表明与现有技术中的类似方法相比，dragon 通过联系较少数量的节点来解决查询。
Distributed query processing is of paramount importance in next-generation distribution services, such as Internet of Things (IoT) and cyber–physical systems. Even if several multi-attribute range queries supports have been proposed for peer-to-peer systems, these solutions must be rethought to fully meet the requirements of new computational paradigms for IoT, like fog computing. This paper proposesdragon, an efficient support for distributed multi-dimensional range query processing targeting efficient query resolution on highly dynamic data. Indragonnodes at the edges of the network collect and publish multi-dimensional data. The nodes collectively manage an aggregation tree storing data digests which are then exploited, when resolving queries, to prune the sub-trees containing few or no relevant matches. Multi-attribute queries are managed by linearizing the attribute space through space filling curves. We extensively analysed different aggregation and query resolution strategies in a wide spectrum of experimental set-ups. We show thatdragonmanages efficiently fast changing data values. Further, we show thatdragonresolves queries by contacting a lower number of nodes when compared to a similar approach in the state of the art.