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A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarity

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Abstract

It is crucial for users to find lots of information with time and geographical tags on the Internet. Therefore, a nearest neighbor query method called STR-kNN is proposed. Using the method of calculating the spatiotemporal similarity between two objects, the spatiotemporal variables of the data object are normalized and mapped to the three-dimensional space. The distance similarity between two data objects in the three-dimensional space is used to approximate their actual spatiotemporal similarity. In this way, a three-dimensional STR-tree index is built for the data object in the three-dimensional space. This index can effectively combine the spatial–temporal variables of the data object, ensuring that each data object is traversed no more than once during query processing. Finally, an accurate searching algorithm of STR-kNN is designed to find up to the top-k query results through a single calculation. In the experiment, when the data volume is 6 million, the query times of STR-kNN, 3DR-k NN, and RT-k NN algorithms are 12 ms, 43 ms, and 55 ms respectively. When k is taken as 50, the query times of the three algorithms are 12 ms, 43 ms, and 55 ms, respectively, indicating that the new algorithm can greatly improve the query efficiency.

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Authors and Affiliations

  1. Information Management Center, Zhengzhou University of Light Industry, Zhengzhou, 450066, China

    Shenyi Qian

  2. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450066, China

    Ziqiao Tian

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  1. Shenyi Qian
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ZT wrote the original draft, and SQ reviewed and editing the final version.

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Correspondence to Shenyi Qian.

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Qian, S., Tian, Z. A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarity. Evol. Intel. 17, 3031–3041 (2024). https://doi.org/10.1007/s12065-024-00926-7

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  • DOI: https://doi.org/10.1007/s12065-024-00926-7

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