Spatial Keyword Group Query: History
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Subjects: Computer Science, Theory & Methods
Contributor:
Liping Zhang
,
Jing Li
,
Song Li

基于位置的服务在日常生活中已普遍使用。空间关键词查询作为基于位置的服务中的重要技术之一，已广泛应用于智能导航系统、空间定位系统等多个领域。学者们对不同类型的空间关键词查询问题进行了深入研究。

  • time-aware
  • exclusion keywords
  • Huffman coding
  • group query
  • spatial keyword query

. Introduction

With the rapid development of Internet technology and sensor technology in recent years, location-based services have been commonly used in daily life. In particular, spatial keyword query, as one of the important technologies in location-based services, has been widely used in many fields, such as intelligent navigation systems and spatial positioning systems. Different types of spatial keyword query problems have been studied in depth by scholars at home and abroad, such as spatial keyword nearest neighbor query problems [1][2][3], TOP-k spatial keyword queries [4][5][6][7][8], inverse nearest neighbor queries [9][10][11][12] and spatial keyword group queries [13][14][15]. In the spatial database, a number of spatial–text objects, also known as points of interest (POI), are stored. A spatial keyword query returns one or some points of interest, which need to meet the various requirements of the query and be the best distance. Among the various branches of spatial keyword queries, the application of spatial keyword group queries in daily life has gradually spread in recent years and attracted the attention of many scholars. For example, when a tourist travels to a certain city, planning the desired location of the hotel needs to include activities such as eating, visiting the park, watching movies and so on for a period of time.
Temporal information is also an indispensable consideration in the field of spatial keyword queries. Since each POI such as stores or parks has its own opening hours, users cannot access the POI during non-opening hours. In the query system or recommendation system, different POIs have different opening hours, for example, the breakfast store is open from 6:30 to 10:00 and the shopping mall is open from 9:30 to 20:00, so for the different needs of different users, it is necessary to return different POI objects. Specifically, when the user wants to eat breakfast at 7:00, the query system should return the above breakfast store instead of the mall. Although there may be restaurants in the mall, the mall’s time does not meet the user’s needs. However, most researchers have focused on textual constraints and spatial constraints and have not considered the impact of temporal constraints in daily life.
Furthermore, with the development of science and technology in society, the needs of users are also increasing. For spatial keyword queries, users may not only want to query to meet the required keyword objects, but may also hope that, in the query process, they can be excluded from containing the user’s exclusion keyword objects. Moreover, some users around the world also have many exclusionary matters due to their religious beliefs and other reasons. However, few studies on spatial keyword querying have considered users’ exclusion preferences. Therefore, spatial keyword querying with exclusion keywords has great research value.

2. 空间关键词组查询

空间关键字组查询问题[13][14][15]作为空间关键词查询的一种特殊形式，在现实生活中有着共同的应用。mCK（m-最接近的关键字）查询的概念最早是在文献中提出的[16].给定一组查询关键字，mCK查询查找m个兴趣点，这组兴趣点的关键字信息共同覆盖查询关键字，最小化组中对象的最大成对距离。然而，Zhang等人。[16]只关注每个对象只包含一个查询关键字的事实，其提出的精确算法对大型数据集的适应性较低。考虑到多个关键词的可能性，Guo et al.[17]放宽了每个对象只有一个关键字的约束，并提出了因子为 （1.15 + ε） 的近似算法。邓等.[18]提出了一个最佳关键字覆盖率问题，这是 mCK 查询的变体。本研究同时考虑了组内距离和关键词权重，并将这两个因素合并到线性成本函数中，从而提出了一种精确的算法来解决这个问题。由于mCK查询的结果在一些应用场景中没有提供足够的支持，文献中提出了一种新的空间关键词查询，称为SK-Cover查询（spatial keyword cover query）[19].此查询考虑了结果集中的对象数，并为此查询问题放置了时间复杂度为 O （logm） 的近似算法。制定了有效的访问策略和剪枝规则，以提高算法的效率和可扩展性。[19].随后，Li et al.[20]研究了空间关键字组查询，并提出了一种参数化近似算法，该算法允许近似比率自适应，用户分配任意查询精度。
文献[13][14][15][16][17][18][19][20]是传统的空间关键词组查询，只关注关键词约束和距离约束的研究。然而，一些研究人员在一定程度上扩展了传统的空间关键字组查询。Singh 等人。[21]考虑用关键字标记并嵌入到向量空间中的对象，以研究多维空间中的多目标查询问题，以获得满足给定关键字集的最接近的点集。在低维空间中，IR 树主要用于支持查询处理。但在高维空间（维数大于 10）中，IR 树的不同 MBR（最小边界矩形）可能包含大量重叠数据，导致索引性能降低。因此，本文研究了高维空间中的索引机制和查询算法，使用随机投影和基于哈希的索引结构，并实现了高可扩展性和加速率。CoSKQ（集体空间关键词查询）查询模型引入了基于mCK的查询点，Gao、Cao和Zhao等人。[22][23][24]研究了路网空间中的CoSKQ查询模型。与大多数研究不同，Zhao等人。[24]在道路网络中提出了一个流行感知聚合关键词，旨在找到一组流行POI（即一个流行区域）。POI覆盖了查询关键字，满足了从每个节点到查询节点之间以及每个节点对之间的距离要求，从而使查询关键字的这些节点的分数之和最大化。为此，提出了一种用于评分的缩放技术来减少搜索空间，并提出了一种冗余计算约简技术来减少查询处理中的冗余计算。Su 等.[25]还研究了路网环境下的集成空间关键词查询，但不同的是它基于基于组的集成查询，即GBCK（group-based collective keyword）。考虑到关键词水平对决策支持的重要性，Zhang等人。[26]提出了Level Aware Set Space Keyword Query（LCSK），以找到一组共同覆盖具有阈值约束和最小空间距离成本的查询关键字的POI。为LCSK设计了一种与该问题相关的精确算法和具有可证明近似的近似算法。为了更好地表达在设定空间中对成本感知和距离约束的关键字查询的更细粒度的偏好，Chan 等人。[27]提出了新的准则，优化了成本函数，设计了精确近似算法。徐， 等.[28]与其他工作不同的是，它从查询点的动力学角度研究了移动集空间关键字查询，并提出了两种基于安全区技术的近似算法。
考虑到时态信息在空间关键词查询中的重要性，一些研究者提出了具有时态感知的查询模型。Chen 等.[29]首先研究了时间感知布尔关键字查询，称为TABSKQ（time-aware Boolean spatial keyword query），并针对该问题模型提出了一种高效的索引结构TA树及其对应的查询算法。索引可以有效地修剪搜索空间，并同时考虑关键字信息和时间信息。Chen 等.[30]提出了两种用于时间感知聚合关键字查询的评估函数，以满足不同类型的查询需求，并都提出了相应的处理算法。Chan 等人。[31]研究了具有时间约束的室内关键词路由模型，即TIKRQ问题（时间约束的室内关键词感知路由查询），并提出了一系列剪枝规则和相应的求解算法。考虑到现有著作大多无法解决拼写错误问题，因此主要集中在交通网络中的时间感知近似集关键词搜索上，Feng等人认为。[32]提出了一种用于查询对象距离剪枝的TDAG树索引，并设计了两种近似算法，以在很大程度上提高处理效率。

This entry is adapted from the peer-reviewed paper 10.3390/ijgi12100438

References

  1. Zhang, L.; Li, S.; Guo, Y.; Hao, X. A Method for k Nearest Neighbor Query of Line Segment in Obstructed Spaces. J. Inf. Process. Syst. 2020, 16, 406–420.
  2. Zhang, L.; Ren, L.; Hao, X.; Li, S. Query Method for Nearest Region of Spatial Line Segment Based on Hilbert Curve Grid. Int. J. Innov. Comput. Inf. Control 2019, 15, 1287–1307.
  3. Yang, R.; Niu, B. Continuous k Nearest Neighbor Queries over Large-Scale Spatial–Textual Data Streams. ISPRS Int. J. Geo-Inf. 2020, 9, 694.
  4. Li, S.; Hu, Y.; Hao, X.; Zhang, L.; Hao, Z. Approximate k-Nearest Neighbor Query of High Dimensional Data Based on Dimension Grouping and Reducing. J. Comput. Res. Dev. 2021, 58, 609–623.
  5. Dias de Almeida, J.P.; Durão, F.A. Personalizing the Top-k Spatial Keyword Preference Query with Textual Classifiers. Expert Syst. Appl. 2020, 162, 113841.
  6. Wang, J.; Xiong, Z.; Han, Q.; Han, X.; Yang, D. Top-k Socially Constrained Spatial Keyword Search in Large SIoT Networks. IEEE Internet Things J. 2022, 9, 9280–9289.
  7. Zhang, D.; Tan, K.-L.; Tung, A.K.H. Scalable Top-k Spatial Keyword Search. In Proceedings of the 16th International Conference on Extending Database Technology—EDBT’13, Genoa, Italy, 18–22 May 2013; ACM Press: Genoa, Italy, 2013; p. 359.
  8. Pan, X.; Yu, Q.-D.; Ma, A.; Sun, Y.; Wu, L.; Guo, J. Efficient algorithm of top-k spatial keyword search with OR semantics. J. Softw. 2020, 31, 3197–3215.
  9. Cheong, O.; Vigneron, A.; Yon, J. Reverse Nearest Neighbor Queries in Fixed Dimension. Int. J. Comput. Geom. Appl. 2011, 21, 179–188.
  10. Allheeib, N.; Islam, M.S.; Taniar, D.; Shao, Z.; Cheema, M.A. Density-Based Reverse Nearest Neighbourhood Search in Spatial Databases. J. Ambient. Intell. Humaniz. Comput. 2021, 12, 4335–4346.
  11. Islam, M.S.; Shen, B.; Wang, C.; Taniar, D.; Wang, J. Efficient Processing of Reverse Nearest Neighborhood Queries in Spatial Databases. Inf. Syst. 2020, 92, 101530.
  12. Pan, X.; Nie, S.; Hu, H.; Yu, P.S.; Guo, J. Reverse Nearest Neighbor Search in Semantic Trajectories for Location-Based Services. IEEE Trans. Serv. Comput. 2022, 15, 986–999.
  13. Cao, X.; Cong, G.; Jensen, C.S.; Ooi, B.C. Collective Spatial Keyword Querying. In Proceedings of the 2011 International Conference on Management of Data—SIGMOD’11, Athens, Greece, 12–16 June 2011; ACM Press: Athens, Greece, 2011; p. 373.
  14. Song, X.; Xu, J.; Zhou, R.; Liu, C.; Zheng, K.; Zhao, P.; Falkner, N. Collective Spatial Keyword Search on Activity Trajectories. Geoinformatica 2020, 24, 61–84.
  15. Chan, H.K.-H.; Long, C.; Wong, R.C.-W. On Generalizing Collective Spatial Keyword Queries. IEEE Trans. Knowl. Data Eng. 2018, 30, 1712–1726.
  16. Zhang, D.; Chee, Y.M.; Mondal, A.; Tung, A.K.H.; Kitsuregawa, M. Keyword Search in Spatial Databases: Towards Searching by Document. In Proceedings of the 2009 IEEE 25th International Conference on Data Engineering, Shanghai, China, 29 March–2 April 2009; IEEE: Shanghai, China, 2009; pp. 688–699.
  17. Guo, T.; Cao, X.; Cong, G. Efficient Algorithms for Answering the M-Closest Keywords Query. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Australia, 31 May–4 June 2015; ACM: Melbourne, Australia, 2015; pp. 405–418.
  18. Deng, K.; Li, X.; Lu, J.; Zhou, X. Best Keyword Cover Search. IEEE Trans. Knowl. Data Eng. 2015, 27, 61–73.
  19. Choi, D.-W.; Pei, J.; Lin, X. Finding the Minimum Spatial Keyword Cover. In Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 16–20 May 2016; IEEE: Helsinki, Finland, 2016; pp. 685–696.
  20. Li, J.; Xu, M. A Parametric Approximation Algorithm for Spatial Group Keyword Queries. IDA 2021, 25, 305–319.
  21. Singh, V.; Zong, B.; Singh, A.K. Nearest Keyword Set Search in Multi-Dimensional Datasets. IEEE Trans. Knowl. Data Eng. 2016, 28, 741–755.
  22. Gao, Y.; Zhao, J.; Zheng, B.; Chen, G. Efficient Collective Spatial Keyword Query Processing on Road Networks. IEEE Trans. Intell. Transport. Syst. 2016, 17, 469–480.
  23. Cao, X.; Chen, L.; Cong, G.; Xiao, X. Keyword-Aware Optimal Route Search. Proc. VLDB Endow. 2012, 5, 1136–1147.
  24. Zhao, S.; Cheng, X.; Su, S.; Shuang, K. Popularity-Aware Collective Keyword Queries in Road Networks. Geoinformatica 2017, 21, 485–518.
  25. Su, S.; Zhao, S.; Cheng, X.; Bi, R.; Cao, X.; Wang, J. Group-Based Collective Keyword Querying in Road Networks. Inf. Process. Lett. 2017, 118, 83–90.
  26. Zhang, P.; Lin, H.; Yao, B.; Lu, D. Level-Aware Collective Spatial Keyword Queries. Inf. Sci. 2017, 378, 194–214.
  27. Chan, H.K.-H.; Liu, S.; Long, C.; Wong, R.C.-W. Cost-Aware and Distance-Constrained Collective Spatial Keyword Query. IEEE Trans. Knowl. Data Eng. 2021, 35, 1324–1336.
  28. Xu, H.; Gu, Y.; Sun, Y.; Qi, J.; Yu, G.; Zhang, R. Efficient Processing of Moving Collective Spatial Keyword Queries. VLDB J. 2020, 29, 841–865.
  29. Chen, G.; Zhao, J.; Gao, Y.; Chen, L.; Chen, R. Time-Aware Boolean Spatial Keyword Queries. IEEE Trans. Knowl. Data Eng. 2017, 29, 2601–2614.
  30. Chen, Z.; Zhao, T.; Liu, W. Time-Aware Collective Spatial Keyword Query. ComSIS 2021, 18, 1077–1100.
  31. Chan, H.K.-H.; Liu, T.; Li, H.; Lu, H. Time-Constrained Indoor Keyword-Aware Routing. In Proceedings of the 17th International Symposium on Spatial and Temporal Databases, online, 23 August 2021; pp. 74–84.
  32. Feng, Z.; Jin, C.; Kim, H.; Cui, X. Time-Aware Approximate Collective Keyword Search in Traffic Networks. Knowl.-Based Syst. 2021, 229, 107367.
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