Multi-view Based Spatial-Keyword Query Processing for Real Estate

  • Xi Duan
  • Liping WangEmail author
  • Shiyu Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11642)


The real estate search web systems such as Zillow, Anjuke, and Lianjia have become very popular in daily life. Generally, the comprehensive query results combined with transportation, health care, education, POIs, etc. are expected, but those surrounding information are rarely utilized in traditional query methods, which thereby restricts the results of the query. In this paper, we address the above limitations and provide a novel multi-view based query method, named KBHR. We investigate feature extraction method and introduce multi-view to represent comprehensive real estate data. The proposed method, KBHR, is based on BHR-tree which is a hybrid indexing structure and a kernel based similarity function developed to rank the query results of multi-view data. We construct experiments and evaluate KBHR on real-world data sets. The experimental results demonstrate the efficiency and effectiveness of our method.


Spatial-keyword query Multi-view data Hybrid indexing structure Kernel function 



This work was partially supported by NSFC 61401155.


  1. 1.
    Hartz, D.K., Gorman, M.T., Rossum, E.: Real-estate information search and retrieval system. In: US (2003)Google Scholar
  2. 2.
    Martinez, L., Contreras, J., Mendoza, R.: INMO: a web architecture for real estate search systems. IEEE Latin Am. Trans. 13(4), 1148–1152 (2015)CrossRefGoogle Scholar
  3. 3.
    Liu, X.P., Wan, C.X., Liu, D.X.: Survey on spatial keyword search. J. Softw. 27(2), 329–347 (2016)MathSciNetGoogle Scholar
  4. 4.
    Chen, L., Cong, G., Jensen, C.S.: Spatial keyword query processing: an experimental evaluation. Proc. VLDB Endow. 6(3), 217–228 (2013)CrossRefGoogle Scholar
  5. 5.
    Cao, X., Cong, G., Jensen, C. S., Ooi, B.C.: Collective spatial keyword querying. In: SIGMOD Conference, pp. 373–384 (2011)Google Scholar
  6. 6.
    Chen, L., Cong, G., Cao, X.: An efficient query indexing mechanism for filtering geo-textual data. In: SIGMOD Conference, pp. 749–760 (2013)Google Scholar
  7. 7.
    Wu, D., Yiu, M.L., Cong, G., Jensen, C.S.: Joint top-k spatial keyword query processing. IEEE Trans. Knowl. Data Eng. 24(10), 1889–1903 (2012)CrossRefGoogle Scholar
  8. 8.
    Zhang, C., Zhang, Y., Zhang, W.: Inverted linear quadtree: efficient top k spatial keyword search. IEEE Trans. Knowl. Data Eng. 28(7), 1706–1721 (2016)CrossRefGoogle Scholar
  9. 9.
    Zhou, Y., Xie, X., Wang, C.: Hybrid index structures for location-based web search. In: International Conference on Information & Knowledge Management, pp. 155–162. ACM (2005)Google Scholar
  10. 10.
    Felipe, I.D., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: IEEE International Conference on Data Engineering, pp. 656–665 (2008)Google Scholar
  11. 11.
    Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. Comput. Sci. (2013)Google Scholar
  12. 12.
    Eaton, E., Desjardins, M., Jacob, S.: Multi-view clustering with constraint propagation for learning with an incomplete mapping between views. In: ACM International Conference on Information & Knowledge Management, pp. 389–398 (2010)Google Scholar
  13. 13.
    Deng, C., Lv, Z., Liu, W.: Multi-view matrix decomposition: a new scheme for exploring discriminative information. In: International Conference on Artificial Intelligence, pp. 3438–3444. AAAI Press (2015)Google Scholar
  14. 14.
    Yuan, N.J., Zheng, Y., Xie, X.: Discovering urban functional zones using latent activity trajectories. IEEE Trans. Knowl. Data Eng. 27(3), 712–725 (2015)CrossRefGoogle Scholar
  15. 15.
    Zheng, Y., Liu, F., Hsieh, H.P.: U-Air: when urban air quality inference meets big data. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1436–1444 (2013)Google Scholar
  16. 16.
    Wang, Y., Cheema, M.A., Lin, X., Zhang, Q.: Multi-manifold ranking: using multiple features for better image retrieval. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 449–460. Springer, Heidelberg (2013). Scholar
  17. 17.
    Dhillon, P.S., Foster, D., Ungar, L.: Multi-view learning of word embeddings via CCA. In: Proceedings of Nips, pp. 199–207 (2011)Google Scholar
  18. 18.
    Krainer, J., Wei, C.: House prices and fundamental value. FRBSF Econ. Lett. (2004)Google Scholar
  19. 19.
    Kamel, I., Faloutsos, C.: Hilbert R-tree: an improved R-tree using fractals. In: International Conference on Very Large Data Bases, pp. 500–509 (1994)Google Scholar
  20. 20.
    Manning, C., Raghavan, P.: Introduction to Information Retrieval, pp. 824–825. Cambridge University Press, Cambridge (2010)Google Scholar
  21. 21.
    Yu, L.H., Du, Y.: Methods and technology of data preprocess in data mining. J. Anhui Vocat. College Electron. Inf. Technol. (2009)Google Scholar
  22. 22.
    Fu, K.S.: Pattern Recognition and Machine Learning, pp. 461–462. Springer, New York (2006)Google Scholar
  23. 23.
    Liu, L.: Normalized discounted cumulated gain (nDCG). Encyclopedia of Database Systems, p. 1920. Springer, Boston (2009). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina

Personalised recommendations