A new spatio–temporal prediction approach based on aggregate queries. (19th March 2013)

• Record Type: Journal Article
• Authors: Feng, Jun; Zhu, Zhonghua; Shi, Yaqing; Xu, Liming
• Is Part Of: International journal of knowledge and web intelligence. Volume 4:Number 1(2013)
• Page Start: 20
• Page End: 33
• Publication Date: 2013-03-19
• Subjects: data streams -- road networks -- aggregate queries -- adaptive multi–dimensional histogram -- AMH&#42 -- -- bucket -- dynamic sketch index -- SAES -- self–adaptive exponential smoothing -- aggregate index architecture -- spatio–temporal prediction -- intelligent partitioning; Information retrieval -- Periodicals; Web site development -- Periodicals; Internet programming -- Periodicals; 004.67805
• Languages: English
• ISSNs: 1755-8255

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).