Time-Aware and Direction-Constrained Collective Spatial Keyword Query

Collective spatial keyword query (CoSKQ) is an important variant of spatial keyword queries and has become a research hotspot. In real life, user behavior usually has a certain directionality, so they may want to obtain the result object that conforms to a specific direction, which is what the direction-constrained query studies. In addition, query time information also plays an important role in location-based query processing. To this end, this paper takes the lead in studying the Time-aware and Direction-constrained Collective Spatial Keyword Query (TDCoSKQ). To facilitate direction-related operations, space objects are organized using the polar coordinate system. Firstly, an efficient space partition method is designed, and on this basis, a new hybrid index structure KRPQT is designed. Based on KRPQT, several pruning strategies are proposed to prune irrelevant regions and objects from the perspective of keyword, time, and direction, and the basic algorithm KRPQB is proposed. To further improve the efficiency of query processing, the possible areas of the result objects are analyzed and shrunk to greatly reduce the number of candidate objects, and three optimization algorithms KRPSW, KRPSW+LFO, and KRPSW+KRPQB are proposed. Then, we discuss how to extend the proposed methods to deal with TDCoSKQ queries with other distance functions and TDCoSKQ queries with weight objects. Finally, the efficiency of the proposed algorithm is verified by simulation experiments.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-03-01
DOI
https://doi.org/10.1109/tits.2024.3523406
Citations
2
Primary Topic
Data Management and Algorithms
Type
article
Field-Weighted Citation Impact
2.97

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article

Time-Aware and Direction-Constrained Collective Spatial Keyword Query

Zhe Feng, Guohui Li, Xiaokun Du, Jianjun Li et al.
2 citations
IEEE Transactions on Intelligent Transportation Systems
Data Management and Algorithms
2.97
article

Time-Aware and Direction-Constrained Collective Spatial Keyword Query

Zhe Feng, Guohui Li, Xiaokun Du, Jianjun Li, Changlong Jin
article en
2 citations

Abstract

Collective spatial keyword query (CoSKQ) is an important variant of spatial keyword queries and has become a research hotspot. In real life, user behavior usually has a certain directionality, so they may want to obtain the result object that conforms to a specific direction, which is what the direction-constrained query studies. In addition, query time information also plays an important role in location-based query processing. To this end, this paper takes the lead in studying the Time-aware and Direction-constrained Collective Spatial Keyword Query (TDCoSKQ). To facilitate direction-related operations, space objects are organized using the polar coordinate system. Firstly, an efficient space partition method is designed, and on this basis, a new hybrid index structure KRPQT is designed. Based on KRPQT, several pruning strategies are proposed to prune irrelevant regions and objects from the perspective of keyword, time, and direction, and the basic algorithm KRPQB is proposed. To further improve the efficiency of query processing, the possible areas of the result objects are analyzed and shrunk to greatly reduce the number of candidate objects, and three optimization algorithms KRPSW, KRPSW+LFO, and KRPSW+KRPQB are proposed. Then, we discuss how to extend the proposed methods to deal with TDCoSKQ queries with other distance functions and TDCoSKQ queries with weight objects. Finally, the efficiency of the proposed algorithm is verified by simulation experiments.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(3)
Minzu University of China (CN), Shandong University (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 11%
Data Management and Algorithms
2.97
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Time-Aware and Direction-Constrained Collective Spatial Keyword Query — Zhe Feng, Guohui Li, et al. · IEEE Transactions on Intelligent Transportation Systems (2025) | TGRS Research Map | TGRS