Title: A new spatio-temporal prediction approach based on aggregate queries

Authors: Jun Feng; Zhonghua Zhu; Yaqing Shi; Liming Xu

Addresses: College of Computer & Information, Hohai University, No. 1 Xikang Road, Nanjing, Jiangsu 210098, China ' College of Computer & Information, Hohai University, No. 1 Xikang Road, Nanjing, Jiangsu 210098, China ' Institute of Science, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China ' College of Computer & Information, Hohai University, No. 1 Xikang Road, Nanjing, Jiangsu 210098, China

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).

Keywords: data streams; road networks; aggregate queries; adaptive multi-dimensional histogram; AMH*; bucket; dynamic sketch index; SAES; self-adaptive exponential smoothing; aggregate index architecture; spatio-temporal prediction; intelligent partitioning.

DOI: 10.1504/IJKWI.2013.052723

International Journal of Knowledge and Web Intelligence, 2013 Vol.4 No.1, pp.20 - 33

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 18 Mar 2013 *

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