A new spatio-temporal prediction approach based on aggregate queries

Jun Feng, Zhonghua Zhu, Yaqing Shi, Liming Xu
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引用次数: 2

Abstract

The prediction of spatio-temporal data streams which is based on aggregate queries has been an important research direction in the research field of databases. More and more methods have been proposed to obtain approximate aggregate results. However, they will consume a lot of time and storage space. This paper proposes Dynamic Sketch DS index by using modified method of Adaptive Multi-dimensional Histogram AMH* to intelligently partition static sketch which can improve the approximate quality of aggregate queries in road networks. Then, based on DS index, this paper proposes a new prediction approach over data streams in road networks using Self-Adaptive Exponential Smoothing SAES.
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一种基于聚合查询的时空预测方法
基于聚合查询的时空数据流预测已成为数据库研究领域的一个重要研究方向。越来越多的方法被提出来获得近似的聚合结果。然而,它们会消耗大量的时间和存储空间。本文采用改进的自适应多维直方图AMH*方法,提出了动态草图DS索引,对静态草图进行智能划分,提高了路网聚合查询的近似质量。在此基础上,提出了一种基于自适应指数平滑的道路网络数据流预测方法。
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