Skip to main content
SpringerLink
Log in

Social-aware spatial keyword top-k group query

  • Published:
Distributed and Parallel Databases Aims and scope Submit manuscript

Abstract

With the increasing popularity of location-based social networking services, information in social networks has become an important basis for analyzing user preferences. However, the existing spatial keyword group query only focuses on the distance constraint between the user groups, and ignores the social relationship between the user and his friends, which may affect the query results. Therefore, in order to meet the diverse query needs of user groups and improve user satisfaction based on information in social networks, this paper proposes a social-aware spatial keyword top-k group query problem. This problem aims to retrieve a set of k groups of POI objects that satisfy the preferences of multiple users, taking into account spatial proximity, social relevance, and keyword constraints. To solve this problem, we first design a rank function to measure the correlation between the query set and the candidate set. Next, in order to improve the query efficiency, we develop a novel hybrid index structure, SAIR-tree, which comprehensively considers the attributes of social, spatial, and textual. Then, we propose an approximate algorithm and an exact algorithm, combining with the pruning strategy, can efficiently search the top-k result set. Finally, experiments on real dataset confirm the efficiency and accuracy of the proposed algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Ahmad, S., Kamal, R., Ali, ME, Qi, J., Scheuermann, P., Tanin, E.: The flexible group spatial keyword query. In: Huang, Z., Xiao, X., Cao, X. (eds.) Databases Theory and Applications—28th Australasian Database Conference, ADC 2017, Brisbane, QLD, Australia, September 25–28, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10538, pp. 3–16. Springer, New York (2017)

  2. Cao, X., Cong, G., Jensen, C.S., Ooi, B.C.: Collective spatial keyword querying. In: Sellis, T.K., Miller, R.J., Kementsietsidis A., Velegrakis, Y. (eds.) Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, June 12–16, 2011, pp. 373–384. ACM, New York (2011)

  3. Cao, X., Cong, G., Guo, T., Jensen, C.S., Ooi, B.C.: Efficient processing of spatial group keyword queries. ACM Trans. Database Syst. 40(2), 13:1–13:48 (2015)

    Article  MathSciNet  Google Scholar 

  4. Chen, L., Shang, S.: Region-based message exploration over spatio-temporal data streams. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27–February 1, 2019, pp. 873–880. AAAI Press (2019)

  5. Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. PVLDB 6(3), 217–228 (2013)

    Google Scholar 

  6. Chen, L., Shang, S., Zheng, K., Kalnis, P.: Cluster-based subscription matching for geo-textual data streams. In: 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, April 8–11, 2019, pp 890–901. IEEE (2019)

  7. Chen, L., Shang, S., Jensen, C.S., Xu, J., Shao, L.: Top-k term publish/subscribe for geo-textual data streams. The VLDB Journal (2020)

  8. Chen, L., Shang, S., Yang, C., Li, J.: Spatial keyword search: a survey. GeoInformatica 24(1), 85–106 (2020b)

    Article  Google Scholar 

  9. Chen, Z., Zhao, T., Liu, W.: Time-aware spatial keyword cover query. Data Knowl. Eng. 122, 81–100 (2019b)

    Article  Google Scholar 

  10. Choi, D., Pei, J., Lin, X.: Finding the minimum spatial keyword cover. In: 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, May 16–20, 2016, pp. 685–696. IEEE Computer Society (2016)

  11. Cong, G., Jensen, C.S.: Querying geo-textual data: Spatial keyword queries and beyond. In: Özcan, F., Koutrika, G., Madden, S. (eds) Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, San Francisco, CA, USA, June 26–July 01, 2016, pp 2207–2212. ACM, New York (2016)

  12. Doytsher, Y., Galon, B., Kanza, Y.: Querying geo-social data by bridging spatial networks and social networks. In: Zhou, X., Lee, W., Peng, W., Xie, X. (eds.), Proceedings of the 2010 International Workshop on Location Based Social Networks, LBSN 2010, November 2, 2010, San Jose, CA, USA, pp 39–46. ACM, New York (2010)

  13. Du, B., Ru, L., Wu, C., Zhang, L.: Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Trans. Geosci. Remote Sens. 57(12), 9976–9992 (2019a)

    Article  Google Scholar 

  14. Du, B., Wei, Q., Liu, R.: An improved quantum-behaved particle swarm optimization for endmember extraction. IEEE Trans. Geosci. Remote Sens. 57(8), 6003–6017 (2019b)

    Article  Google Scholar 

  15. Ekomie, H.B., Yao, K., Li, J., Li, G., Li, Y.: Group top-k spatial keyword query processing in road networks. In: Benslimane, D., Damiani, E., Grosky, W.I., Hameurlain, A., Sheth, A.P., Wagner, R.R. (eds.) Database and Expert Systems Applications—28th International Conference, DEXA 2017, Lyon, France, August 28–31, 2017, Proceedings, Part I. Lecture Notes in Computer Science, vol. 10438, pp 395–408. Springer, Berlin (2017)

  16. Goonetilleke, O., Koutra, D., Liao, K., Sellis, T.: On effective and efficient graph edge labeling. Distrib. Parallel Databases 37(1), 5–38 (2019)

    Article  Google Scholar 

  17. Guo, T., Cao, X., Cong, G.: Efficient algorithms for answering the m-closest keywords query. In: Sellis, T.K., Davidson, S.B., Ives, Z.G. (eds.) Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31–June 4, 2015, pp. 405–418. ACM, New York (2015)

  18. Jensen, C.S.: Spatial keyword querying of geo-tagged web content. In: 7th International Workshop on Ranking in Databases (co-located with VLDB 2013), DBRank 2013, Riva del Garda, Italy—August 30–30, 2013, pp 1:1–1:4. ACM, New York (2013)

  19. Kumar, S., Madria, S., Linderman, M.: M-grid: a distributed framework for multidimensional indexing and querying of location based data. Distrib. Parallel Databases 35(1), 55–81 (2017)

    Article  Google Scholar 

  20. Li, M., Chen, L., Cong, G., Gu, Y., Yu, G.: Efficient processing of location-aware group preference queries. In: Mukhopadhyay, S., Zhai, C., Bertino, E., Crestani, F., Mostafa, J., Tang, J., Si, L., Zhou, X., Chang, Y., Li, Y., Sondhi, P. (eds.), Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24–28, 2016, pp 559–568. ACM, New York (2016)

  21. Li, X., Du, B., Xu, C., Zhang, Y., Zhang, L., Tao, D.: Robust learning with imperfect privileged information. Artif. Intell. 282(103), 246 (2020)

    MathSciNet  MATH  Google Scholar 

  22. Li, Y., Wu, D., Xu, J., Choi, B., Su, W.: Spatial-aware interest group queries in location-based social networks. Data Knowl. Eng. 92, 20–38 (2014)

    Article  Google Scholar 

  23. Li, Y., Chen, R., Xu, J., Huang, Q., Hu, H., Choi, B.: Geo-social k-cover group queries for collaborative spatial computing. In: 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, May 16–20, 2016, pp. 1510–1511. IEEE Computer Society (2016)

  24. Long, C., Wong, R.C., Wang, K., Fu, A.W.: Collective spatial keyword queries: a distance owner-driven approach. In: Ross, K.A., Srivastava, D., Papadias, D. (eds.) Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, New York, NY, USA, June 22–27, 2013, pp. 689–700. ACM, New York (2013)

  25. Nishio, S., Amagata, D., Hara, T.: Geo-social keyword top-k data monitoring over sliding window. In: Benslimane, D., Damiani, E., Grosky, W.I., Hameurlain, A., Sheth, A.P., Wagner, R.R. (eds) Database and Expert Systems Applications—28th International Conference, DEXA 2017, Lyon, France, August 28–31, 2017, Proceedings, Part I. Lecture Notes in Computer Science, vol. 10438, pp. 409–424. Springer, Berlin (2017)

  26. Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. 23(3), 449–468 (2014)

    Article  Google Scholar 

  27. Shang, S., Chen, L., Jensen, C.S., Wen, J., Kalnis, P.: Searching trajectories by regions of interest. IEEE Trans. Knowl. Data Eng. 29(7), 1549–1562 (2017)

    Article  Google Scholar 

  28. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Parallel trajectory similarity joins in spatial networks. VLDB J. 27(3), 395–420 (2018)

    Article  Google Scholar 

  29. Shang, S., Chen, L., Zheng, K., Jensen, C.S., Wei, Z., Kalnis, P.: Parallel trajectory-to-location join. IEEE Trans. Knowl. Data Eng. 31(6), 1194–1207 (2019)

    Article  Google Scholar 

  30. Skovsgaard, A., Jensen, C.S.: Finding top-k relevant groups of spatial web objects. VLDB J. 24(4), 537–555 (2015)

    Article  Google Scholar 

  31. Sohail, A., Hidayat, A., Cheema, M.A., Taniar, D.: Location-aware group preference queries in social-networks. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds.) Databases Theory and Applications—29th Australasian Database Conference, ADC 2018, Gold Coast, QLD, Australia, May 24–27, 2018, Proceedings. Lecture Notes in Computer Science, vol. 10837, pp. 53–67. Springer, Berlin (2018)

  32. Soudani, N.M., Fatemi, A., Nematbakhsh, M.: An investigation of big graph partitioning methods for distribution of graphs in vertex-centric systems. Distrib. Parallel Databases 38(1), 1–29 (2020)

    Article  Google Scholar 

  33. Su, S., Zhao, S., Cheng, X., Bi, R., Cao, X., Wang, J.: Group-based collective keyword querying in road networks. Inf. Process. Lett. 118, 83–90 (2017)

    Article  MathSciNet  Google Scholar 

  34. Wang, Z., Du, B., Guo, Y.: Domain adaptation with neural embedding matching. IEEE Trans. Neural Netw. Learn. Syst. (2019)

  35. Wu, D., Li, Y., Choi, B., Xu, J.: Social-aware top-k spatial keyword search. In: Zaslavsky, A.B., Chrysanthis, P.K., Becker, C., Indulska, J., Mokbel, M.F., Nicklas, D., Chow, C. (eds.) IEEE 15th International Conference on Mobile Data Management, MDM 2014, Brisbane, Australia, July 14–18, 2014, vol. 1, pp 235–244. IEEE Computer Society (2014)

  36. Wu, J., Cai, Z., Zeng, S., Zhu, X.: Artificial immune system for attribute weighted naive bayes classification. In: The 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, TX, USA, August 4–9, 2013, pp. 1–8. IEEE (2013)

  37. Wu, J., Hong, Z., Pan, S., Zhu, X., Zhang, C., Cai, Z.: Multi-graph learning with positive and unlabeled bags. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 217–225. SIAM (2014)

  38. Wu, J., Zhu, X., Zhang, C., Yu, P.S.: Bag constrained structure pattern mining for multi-graph classification. IEEE Trans. Knowl. Data Eng. 26(10), 2382–2396 (2014)

    Article  Google Scholar 

  39. Wu, J., Pan, S., Zhu, X., Cai, Z.: Boosting for multi-graph classification. IEEE Trans. Cybern. 45(3), 416–429 (2015)

    Article  Google Scholar 

  40. Yang, C., Chen, L., Shang, S., Zhu, F., Liu, L., Shao, L.: Toward efficient navigation of massive-scale geo-textual streams. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10–16, 2019, ijcai.org, pp. 4838–4845 (2019)

  41. Yang, D., Shen, C., Lee, W., Chen, M.: On socio-spatial group query for location-based social networks. In: Yang, Q., Agarwal, D., Pei, J. (eds.) The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’12, Beijing, China, August 12–16, 2012, pp 949–957. ACM, New York (2012)

  42. Yao, K., Li, J., Li, G., Luo, C.: Efficient group top-k spatial keyword query processing. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) Web Technologies and Applications—18th Asia-Pacific Web Conference, APWeb 2016, Suzhou, China, September 23–25, 2016. Proceedings, Part I. Lecture Notes in Computer Science, vol. 9931, pp. 153–165. Springer, New York (2016)

  43. Yuan, Y., Wang, G., Wang, H., Chen, L.: Efficient subgraph search over large uncertain graphs. PVLDB 4(11), 876–886 (2011)

    Google Scholar 

  44. Yuan, Y., Wang, G., Chen, L., Wang, H.: Efficient subgraph similarity search on large probabilistic graph databases. PVLDB 5(9), 800–811 (2012)

    Google Scholar 

  45. Yuan, Y., Wang, G., Chen, L., Wang, H.: Efficient keyword search on uncertain graph data. IEEE Trans. Knowl. Data Eng. 25(12), 2767–2779 (2013)

    Article  Google Scholar 

  46. Yuan, Y., Wang, G., Chen, L., Wang, H.: Graph similarity search on large uncertain graph databases. VLDB J. 24(2), 271–296 (2015a)

    Article  Google Scholar 

  47. Yuan, Y., Wang, G., Xu, J.Y., Chen, L.: Efficient distributed subgraph similarity matching. VLDB J. 24(3), 369–394 (2015b)

    Article  Google Scholar 

  48. Yuan, Y., Lian, X., Chen, L., Sun, Y., Wang, G.: Rsknn: knn search on road networks by incorporating social influence. IEEE Trans. Knowl. Data Eng. 28(6), 1575–1588 (2016)

    Article  Google Scholar 

  49. Yuan, Y., Lian, X., Chen, L., Yu, J.X., Wang, G., Sun, Y.: Keyword search over distributed graphs with compressed signature. IEEE Trans. Knowl. Data Eng. 29(6), 1212–1225 (2017)

    Article  Google Scholar 

  50. Zhao, J., Gao, Y., Chen, G., Chen, R.: Why-not questions on top-k geo-social keyword queries in road networks. In: 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16–19, 2018, pp. 965–976. IEEE Computer Society (2018)

  51. Zhu, Q., Du, B., Yan, P.: Boundary-weighted domain adaptive neural network for prostate MR image segmentation. IEEE Trans Med Imaging 39(3), 753–763 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This research is partially supported by the National Natural Science Foundation of China under Grant Nos. 61672145, 61702086, China Postdoctoral Science Foundation under Grant No. 2018M631806, Natural Science Foundation of Liaoning Province of China under Grant No. 20180550260, Scientific Research Foundation of Liaoning Province under Grant Nos. L2019001, L2019003, Doctoral Business and Innovation Launching Plan of Yingkou City under Grant No. QB-2019-16.

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning, China

    Xiangguo Zhao & Hong Huang

  2. College of Electrical Engineering, Yingkou Institute of Technology, Yingkou, 115014, Liaoning, China

    Zhen Zhang

  3. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, China

    Xin Bi

Authors
  1. Xiangguo Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangguo Zhao.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Informed consent

Informed consent was obtained from all individual participants.

Research involving human and animal rights

This article does not contain any studies involving human participants and/or animals by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, X., Zhang, Z., Huang, H. et al. Social-aware spatial keyword top-k group query. Distrib Parallel Databases 38, 601–623 (2020). https://doi.org/10.1007/s10619-020-07292-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10619-020-07292-0

Keywords

Search

Navigation