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Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Title of Paper:Spatial index for uncertain time series

Journal:J. Compt. Inf. Technol.

Abstract:A search for patterns in uncertain time series is time-expensive in today's large databases using the currently available methods. To accelerate the search process for uncertain time series data, in this paper, we explore a spatial index structure, which uses uncertain information stored in minimum bounding rectangle and ameliorates the general prune/search process along the path from the root to leaves. To get a better performance, we normalize the uncertain time series using the weighted variance before the prune/hit process. Meanwhile, we add two goodness measures with respect to the variance to improve the robustness. The extensive experiments show that, compared with the primitive probabilistic similarity search algorithm, the prune/hit process of the spatial index can be more efficient and robust using the specific preprocess and variant index operations with just a little loss of accuracy. © 2018 University of Zagreb.

ISSN No.:1330-1136

Translation or Not:no

Date of Publication:2018-01-01

Co-author:Zheng, Diwei,Wang, Yu

Correspondence Author:Zheng, Diwei,yanli