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MCOPS-SPM: Multi-Constrained Optimized Path Selection Based Spatial Pattern Matching in Social Networks

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Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications (CloudComp 2019, SmartGift 2019)

Abstract

In this paper, we study the multi-constrained optimized path selection based spatial pattern matching in Location-Based Social Network (MCOPS-SPM). Given a set D including spatial objects (each with a social identity and a social reputation) and social relationships (e.g., trust degree, social intimacy) between them. We aim at finding all connections (paths) of objects from D that match a user-specified multi-constraints spatial pattern P. A pattern P is a complex network where vertices represent spatial objects, and edges denote social relationships between them. The MCOPS-SPM query returns all the instances that satisfy P. Answering such queries is computationally intractable, and we propose algorithms to solve the multi-constrained optimized path matching problem and guide the join order of the paths in the query results. An extensive empirical study over real-world datasets has demonstrated the effectiveness and efficiency of our approach.

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References

  1. Zhang, D., et al.: Keyword search in spatial databases: towards searching by document. In: ICDE, pp. 688–699. IEEE (2009)

    Google Scholar 

  2. Guo, T., Cao, X., Cong, G.: Efficient algorithms for answering the m-closest keywords query. In: SIGMOD, pp. 405–418. ACM (2015)

    Google Scholar 

  3. Deng, K., Li, X., Lu, J., Zhou, X.: Best keyword cover search. TKDE 27(1), 61–73 (2015)

    Google Scholar 

  4. Choi, D., Pei, J., Lin, X.: Finding the minimum spatial keyword cover. In: ICDE, pp. 685–696. IEEE (2016)

    Google Scholar 

  5. Rocha-Junior, J.B., Gkorgkas, O., Jonassen, S., Nørvåg, K.: Efficient processing of top-k spatial keyword queries. In: Pfoser, D., et al. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 205–222. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22922-0_13

    Chapter  Google Scholar 

  6. Pengfei, Z.: Research on related issues of spatial keyword query. Zhejiang University (2018)

    Google Scholar 

  7. Fang, Y., Cheng, R., Cong, G., Mamoulis, N., Li, Y.: On spatial pattern matching, pp. 293–304 (2018). https://doi.org/10.1109/icde.2018.00035

  8. Fan, W., Li, J., Ma, S., Tang, N., Wu, Y., Wu, Y.: Graph pattern matching: from intractable to polynomial time. In: VLDB 2010, pp. 264–275 (2010)

    Article  Google Scholar 

  9. Cheng, T.S., Gadia, S.K.: A Pattern matching language for spatio-temporal databases (1994)

    Google Scholar 

  10. Cheng, J., Yu, J.X., Ding, B., Yu, P.S., Wang, H.: Fast graph pattern matching. In: ICDE (2008)

    Google Scholar 

  11. Carletti, V., et al.: Challenging the time complexity of exact subgraph isomorphism for huge and dense graphs with VF3. TPAMI (2017)

    Google Scholar 

  12. Zhang, P., Lin, H., Yao, B., et al.: Level-aware collective spatial keyword queries. Inf. Sci. 378, 194–214 (2017)

    Article  MathSciNet  Google Scholar 

  13. Hariharan, R., Hore, B., Li, C., Mehrotra, S.: Processing spatial-keyword (SK) queries in geographic information retrieval (GIR) systems. In: SSDBM, p. 16 (2007)

    Google Scholar 

  14. Cary, A., Wolfson, O., Rishe, N.: Efficient and scalable method for processing top-k spatial Boolean queries. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 87–95. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13818-8_8

    Chapter  Google Scholar 

  15. Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1), 337–348 (2009)

    Google Scholar 

  16. Khodaei, A., Shahabi, C., Li, C.: Hybrid indexing and seamless ranking of spatial and textual features of web documents. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6261, pp. 450–466. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15364-8_37

    Chapter  Google Scholar 

  17. Wu, D., Yiu, M.L., Cong, G., et al.: Joint top-K spatial keyword query processing. IEEE Trans. Knowl. Data Eng. 24, 1889–1903 (2012)

    Article  Google Scholar 

  18. Zheng, B., Zheng, K., Jensen, C.S., et al.: Answering why-not group spatial keyword queries. IEEE Trans. Knowl. Data Eng. 32, 26–39 (2018)

    Article  Google Scholar 

  19. Qian, Z., Xu, J., Zheng, K., et al.: Semantic-aware top-k spatial keyword queries. World Wide Web Internet Web Inf. Syst. 21(3), 573–594 (2018)

    Article  Google Scholar 

  20. Mamoulis, N., Papadias, D.: Multiway spatial joins. TODS 26(4), 424–475 (2001)

    Article  Google Scholar 

  21. Zou, L., Chen, L., Ozsu, M.T.: Distance-join: pattern match query in a large graph database. PVLDB 2(1), 886–897 (2009)

    Google Scholar 

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

    Google Scholar 

  23. Tong, H., Faloutsos, C., Gallagher, B., Eliassi-Rad, T.: Fast best-effort pattern matching in large attributed graphs. In: KDD (2007)

    Google Scholar 

  24. Zou, L., Chen, L., Ozsu, M.T.: Distance-join: pattern match query in a large graph database. In: VLDB (2009)

    Article  Google Scholar 

  25. Jing, Y., Yanbing, L., Zhang, Y., Mengya, L., Jianlong, T., Li, G.: Summary of large-scale graph data matching technology. Comput. Res. Dev. 52(02), 391–409 (2015)

    Google Scholar 

  26. Henzinger, M.R., Henzinger, T., Kopke, P.: Computing simulations on fifinite and infifinite graphs. In: FOCS 1995 (1995)

    Google Scholar 

  27. Sokolsky, O., Kannan, S., Lee, I.: Simulation-based graph similarity. In: Tools and Algorithms for the Construction and Analysis of Systems (2006)

    Google Scholar 

  28. Liu, G., Zheng, K., Liu, A., et al.: MCS-GPM: multi-constrained simulation based graph pattern matching in contextual social graphs. IEEE Trans. Knowl. Data Eng. 30, 1050–1064 (2017)

    Article  Google Scholar 

  29. Liu, G., Wang, Y., Orgun, M.A., et al.: A heuristic algorithm for trust-oriented service provider selection in complex social networks. In: IEEE International Conference on Services Computing. IEEE Computer Society (2010)

    Google Scholar 

  30. Berger, P., Luckmann, T.: The Social Construction of Reality: A Treatise in the Sociology of Knowledge. Anchor Books, New York (1966)

    Google Scholar 

  31. Zobel, J., Moffat, A.: Inverted fifiles for text search engines. ACM Comput. Surv. 38(2), 56 (2006)

    Article  Google Scholar 

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Acknowledgments

The work was supported by Natural Science Foundation of Shandong Province (No. ZR2016FQ10), National Natural Science Foundation of China (No. 6167126, No. 61802217), Key Research and Development Program of Shandong Province (No. 2016GGX101007).

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Authors and Affiliations

  1. Qingdao University of Science and Technology, Qingdao, 266061, Shandong, China

    Ying Guo, Lianzhen Zheng & Yuhan Zhang

  2. Macquarie University, Sydney, Australia

    Guanfeng Liu

Authors
  1. Ying Guo
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  2. Lianzhen Zheng
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  3. Yuhan Zhang
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  4. Guanfeng Liu
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Corresponding author

Correspondence to Ying Guo .

Editor information

Editors and Affiliations

  1. Macquarie University, Sydney, NSW, Australia

    Xuyun Zhang

  2. Macquarie University, Sydney, NSW, Australia

    Guanfeng Liu

  3. Pace University, New York, NY, USA

    Meikang Qiu

  4. James Cook University, Cairns, QLD, Australia

    Wei Xiang

  5. James Cook University, Smithfield, QLD, Australia

    Tao Huang

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Guo, Y., Zheng, L., Zhang, Y., Liu, G. (2020). MCOPS-SPM: Multi-Constrained Optimized Path Selection Based Spatial Pattern Matching in Social Networks. In: Zhang, X., Liu, G., Qiu, M., Xiang, W., Huang, T. (eds) Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. CloudComp SmartGift 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-48513-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-48513-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48512-2

  • Online ISBN: 978-3-030-48513-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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