A personalized query method for spatial keywords in indoor environments

Abstract

With the rapid development of indoor Location Based Services (LBS), a growing volume of textual data is being generated in indoor environments. Consequently, indoor spatial keyword query holds significant potential for development in the coming years. However, existing methods for indoor spatial keyword queries often neglect the personalized needs of users. To solve this problem, we propose an Indoor Spatial Keyword Personalized Query (ISKPQ) method. First, a novel index structure called ISKIR-tree has been designed. This index integrates Hilbert encoding techniques and introduces Bloom filters and distance matrices to enhance the efficiency of processing indoor spatial keyword problems. Subsequently, an efficient pruning algorithm based on the ISKIR-tree index is proposed to refine the dataset effectively. Finally, a comprehensive scoring function that considers text similarity, spatial proximity, and user preferences is introduced to score and rank the pruned data points, thereby filtering out the optimal query results that meet users’ personalized requirements. Theoretical analysis and experimental studies demonstrate the outstanding performance of the proposed method in terms of both efficiency and accuracy.

Keywords

Indoor query / Indoor space / Indoor spatial keyword index / Personalized query / Spatial keyword query

References

[1] Basiri A, Lohan ES, Moore T, Winstanley A, Peltola P, Hill C, Amirian P. Indoor location based services challenges, requirements and usability of current solutions. Computer Science Review, 2017, 24: 1-12

[2] Chan HK, Liu S, Long C, Wong RC. Cost-aware and distance-constrained collective spatial keyword query. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(2): 1324-1336

[3] Chan, H. K., Liu, T., Li, H., & Lu, H. (2021). Time-constrained indoor keyword-aware routing. In Proceedings of the 17th International Symposium on Spatial and Temporal Databases (pp. 74–84). New York.

[4] Chen Z, Chen L, Cong G, Jensen CS. Location-and keyword-based querying of geo-textual data: a survey. The VLDB Journal, 2021, 30(4): 603-640

[5] Dias, d. A., P., J., & Durao, F. A. (2020). Personalizing the top-k spatial keyword preference query with textual classifiers. Expert Systems with Applications, 162, 113841.

[6] Dong Y, Xiao C, Chen H, Yu JX, Takeoka K, Oyamada M, Kitagawa H. Continuous top-k spatial-keyword search on dynamic objects. The VLDB Journal, 2021, 30(2): 141-161

[7] Feng, Z., Liu, T., Li, H., Lu, H., Shou, L., & Xu, J. (2020). Indoor top-k keyword-aware routing query. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1213–1224). Dallas.

[8] Gao Y, Zhao J, Zheng B, Chen G. Efficient collective spatial keyword query processing on road networks. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(2): 469-480

[9] Hamilton, C. H. & Rau-Chaplin, A. (2007). Compact hilbert indices for multi-dimensional data (pp. 139–146). in First International Conference on Complex, Intelligent and Software Intensive Systems (CISIS’07), Vienna.

[10] Liu T, Feng Z, Li H, Lu H, Cheema MA, Cheng H, Xu J. Towards indoor temporal-variation aware shortest path query. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(1): 998-1012

[11] Liu X, Wan C, Liu D, Liao G. Survey on spatial keyword search. Journal of Software, 2016, 27(2): 329-347

[12] Lü G, Batty M, Strobl J, Lin H, Zhu A, Chen M. Reflections and speculations on the progress in geographic information systems (gis): A geographic perspective. International journal of geographical information science, 2019, 33(2): 346-367

[13] Pan X, Wu L, Long F, Ma A. Exploiting user behavior learning for personalized trajectory recommendations. Frontiers of Computer Science, 2022, 16: 1-12

[14] Pan X, Yu D, Ma A, Sun Y, Wu L, Guo J. Efficient algorithm of top-k spatial keyword search with or semantics. Journal of Software, 2020, 31(10): 3197-3215

[15] Psyllidis A, Gao S, Hu Y, Kim E, McKenzie G, Purves R, Yuan M, Andris C. Points of interest (poi): A commentary on the state of the art, challenges, and prospects for the future. Computational Urban Science, 2022, 2(1): 20

[16] Qi J, Jia X, Luo M, Feng Q. A privacy-aware k-nearest neighbor query scheme for location-based services. IEEE Internet of Things Journal, 2024, 11(6): 10831-10842

[17] Ramos NM, Almeida RM, Simões ML, Pereira PF. Knowledge discovery of indoor environment patterns in mild climate countries based on data mining applied to in-situ measurements. Sustainable cities and society, 2017, 30: 37-48

[18] Shao Z, Cheema MA, Taniar D, Lu H, Yang S. Efficiently processing spatial and keyword queries in indoor venues. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(9): 3229-3244

[19] Singh S, Kumar V. Modelling the determinants for sustainable smart city through interpretive structure modelling and analytic hierarchy process. Computational Urban Science, 2024, 4(1): 16

[20] Tan Z, Yu H, Wei W, Liu J. Top-k interesting preference rules mining based on maxclique. Expert Systems with Applications, 2020, 143: 113043

[21] Tong Q, Miao Y, Li H, Liu X, Deng RH. Privacy-preserving ranked spatial keyword query in mobile cloud-assisted fog computing. IEEE Transactions on Mobile Computing, 2023, 22(6): 3604-3618

[22] Wang J, Xiong Z, Han Q, Han X, Yang D. Top-k socially constrained spatial keyword search in large siot networks. IEEE Internet of Things Journal, 2022, 9(12): 9280-9289

[23] Yang Y, Miao Y, Ying Z, Ning J, Meng X, Choo KR. Privacy-preserving threshold spatial keyword search in cloud-assisted IIoT. IEEE Internet of Things Journal, 2022, 9(18): 16990-17001

[24] Zeng J, Yu Y, Wen J, Jiang W, Cheng L. Personalized dynamic attention multi-task learning model for document retrieval and query generation. Expert Systems with Applications, 2023, 213: 119026

[25] Zhang, L., Li, J., & Li, S. (2023a). Research on approximate spatial keyword group queries based on differential privacy and exclusion preferences in road networks. ISPRS International Journal of Geo-Information, 12(12), 480.

[26] Zhang, L., Li, J., & Li, S. (2023b). Research on time-aware group query method with exclusion keywords. ISPRS International Journal of Geo-Information, 12(10), 438.

[27] Zhang, S., Ray, S., Lu, R., Guan, Y., Zheng, Y., & Shao, J. (2022). Toward privacy-preserving aggregate reverse skyline query with strong security. IEEE Transactions on Information Forensics and Security, 17, 2538–2552.

[28] Zhong Y, Li J, Zhu S. Continuous spatial keyword search with query result diversifications. World Wide Web, 2023, 26(4): 1935-1948

[29] Zhu H, Liu W, Yin J, Xu J, Lee W. Towards keyword-based geo-social group query services. IEEE Transactions on Services Computing, 2023, 16(1): 670-683

Funding

National Natural Science Foundation of China(62072136)

Natural Science Foundation of Heilongjiang Province(LH2023F031)

National Key Research and Development Program(CN)(2020YFB1710200)