Original scientific paper

https://doi.org/10.20532/cit.2018.1004248

Spatial Index for Uncertain Time Series

Diwei Zheng orcid.org/0000-0003-2667-118X ; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211102, China
Li Yan orcid.org/0000-0002-1881-3128 ; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211102, China
Yu Wang ; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211102, China

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.

Keywords

time series; spatial index; uncertainty; varying distance threshold

Hrčak ID:

213691

URI

https://hrcak.srce.hr/213691

Publication date:

18.12.2018.

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