Collective Spatial Keyword Query Based on Time-Distance Constrained and Cost Aware

Li Song; Cao Wenqi; Hao Xiaohong; Zhang Liping; Hao Zhongxiao

doi:10.7544/issn1000-1239.202330815

Journal of Computer Research and Development > 2025 > 62(3): 808-819. > DOI: 10.7544/issn1000-1239.202330815 CSTR: 32373.14.issn1000-1239.202330815

Li Song, Cao Wenqi, Hao Xiaohong, Zhang Liping, Hao Zhongxiao. Collective Spatial Keyword Query Based on Time-Distance Constrained and Cost Aware[J]. Journal of Computer Research and Development, 2025, 62(3): 808-819. DOI: 10.7544/issn1000-1239.202330815

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Collective Spatial Keyword Query Based on Time-Distance Constrained and Cost Aware

School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080

Funds: The work was supported by the National Natural Science Foundation of China (62072136), the Key Research and Development Project of Heilongjiang Province (JD2023SJ20), and the National Key Research and Development Program of China (2020YFB1710200).

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Author Bio:
Li Song: born in 1977. PhD, professor. Member of CCF. His main research interests include spatial and spatio-temporal database, data mining, and information data security

Cao Wenqi: born in 1999. Master. Her main research interests include spatial database and data mining

Hao Xiaohong: born in 1969. Master，senior experimentalist master. Her main research interest includes spatial database

Zhang Liping: born in 1976. Master，associate professor. Her main research interests include spatial and spatio-temporal database, and information data security

Hao Zhongxiao: born in 1940. PhD, professor. His main research interests include relational database, null database, acyclic database, active database, and spatial database
Received Date: October 10, 2023
Revised Date: July 01, 2024
Accepted Date: August 08, 2024
Available Online: August 15, 2024

Graphical Abstract

Abstract

Abstract

Collective spatial keyword queries play an important role in the fields such as spatial databases, location services, intelligent recommendations, and group intelligence perception. The existing collective spatial keyword query methods do not consider the problem of requiring time-distance constrained and cost aware, and cannot meet the query needs of most users under time-distance constrained. Existing research results have significant limitations. To make up for the shortcomings of existing methods, collective spatial keyword query based on time-distance constrained and cost aware (called TDCCA-CoSKQ) is proposed. To address the issue of not being able to include both keyword information and time information in existing indexes, the TDCIR-Tree index is proposed, which combines inverted files and time attribute label files. TDCIR-Tree can reduce the cost of query calculation. TDCCA_PP algorithm is proposed to address the issue of subsequent screening of collections that meet query criteria for TDCCA-CoSKQ, including TDCCAPruning1, TDCCAPermutation, and TDCCAPruning2, and it can improve the efficiency of keyword queries. The TDC cost function and its corresponding sorting algorithm are proposed. The TDC cost function is composed of distance cost and time cost, which includes independent variable coefficients representing user preference α and β, and it can increase users’ freedom of choice. The problem of existing cost functions not meeting the collective spatial keyword query based on time-distance constrained and cost aware is effectively solved. Theoretical research and experiments have shown that the proposed method has good efficiency and accuracy.
- collective spatial keyword query,
- time-distance constrained,
- cost aware,
- inverted files,
- time attribute label files

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References (23)

References

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