Skip to main content
SpringerLink
Log in

Discovery of accessible locations using region-based geo-social data

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Geo-social data plays a significant role in location discovery and recommendation. In this light, we propose and study a novel problem of discovering accessible locations in spatial networks using region-based geo-social data. Given a set Q of query regions, the top-k accessible location discovery query (k ALDQ) finds k locations that have the highest spatial-density correlations to Q. Both the spatial distances between locations and regions and the POI (point of interest) density within the regions are taken into account. We believe that this type of k ALDQ query can bring significant benefit to many applications such as travel planning, facility allocation, and urban planning. Three challenges exist in k ALDQ: (1) how to model the spatial-density correlation practically, (2) how to prune the search space effectively, and (3) how to schedule the searches from multiple query regions. To tackle the challenges and process k ALDQ effectively and efficiently, we first define a series of spatial and density metrics to model the spatial-density correlation. Then we propose a novel three-phase solution with a pair of upper and lower bounds of the spatial-density correlation and a heuristic scheduling strategy to schedule multiple query regions. Finally, we conduct extensive experiments on real and synthetic spatial data to demonstrate the performance of the developed solutions.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

Notes

  1. http://maps.google.com/

  2. http://map.baidu.com/

  3. http://www.mapquest.com

  4. https://twitter.com/

  5. https://www.flickr.com/

  6. https://foursquare.com/

  7. http://www.cs.utah.edu/~lifeifei/SpatialDataset.htm

References

  1. Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: VLDB, pp. 853–864 (2005)

  2. Chen, Z., Cafarella, M., Chen, J., Prevo, D., Zhuang, J.: Senbazuru: A prototype spreadsheet database management system. PVLDB 6(12), 1202–1205 (2013)

    Google Scholar 

  3. Chen, Z., Cafarella, M. J.: Integrating spreadsheet data via accurate and low-effort extraction. In: SIGKDD, pp. 1126–1135 (2014)

  4. Chen, Z., Cafarella, M. J., Jagadish, H. V.: Long-tail vocabulary dictionary extraction from the Web. In: WSDM, pp. 625–634 (2016)

  5. Chen, Z., Shen, H., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: an efficiency study. In: SIGMOD, pp. 255–266. ACM (2010)

  6. Dai, J., Yang, B., Guo, C., Jensen, C.S., Hu, J.: Path cost distribution estimation using trajectory data. PVLDB 10(3), 85–96 (2017)

    Google Scholar 

  7. Dijkstra, E. W.: A note on two problems in connection with graphs. Numerische Math 1, 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  8. Ding, Z., Yang, B., Chi, Y., Guo, L: Enabling smart transportation systems: A parallel spatio-temporal database approach. IEEE Trans. Comput. 65(5), 1377–1391 (2016)

    Article  MathSciNet  Google Scholar 

  9. Ding, Z., Yang, B., Gu̇ting, R. H., Li, Y.: Network-matched trajectory-based moving-object database: Models and applications. IEEE Trans. Intell. Transp. Syst. 16 (4), 1918–1928 (2015)

    Article  Google Scholar 

  10. Guo, C., Jensen, C. S., Yang, B.: Towards total traffic awareness. SIGMOD Record 43(3), 18–23 (2014)

    Article  Google Scholar 

  11. Guo, C., Yang, B., Andersen, O., Jensen, C. S., Torp, K.: Ecosky: Reducing vehicular environmental impact through eco-routing. In: ICDE, pp. 1412–1415 (2015)

  12. Guo, D., Zhu, Y., Xu, W., Shang, S., Ding, Z.: How to find appropriate automobile exhibition halls: Towards a personalized recommendation service for auto show. Neurocomputing 213, 95–101 (2016)

    Article  Google Scholar 

  13. Han, J., Zheng, K., Sun, A., Shang, S., Wen, J.: Discovering neighborhood pattern queries by sample answers in knowledge base. In: ICDE, pp. 1014–1025 (2016)

  14. Hu, S., Wen, J., Dou, Z., Shang, S.: Following the dynamic block on the Web. World Wide Web 19(6), 1077–1101 (2016)

    Article  Google Scholar 

  15. Li, J., Wang, Y., Zhong, Y., Guo, D., Zhu, S.: Aggregate location recommendation in dynamic transportation networks. World Wide Web Journal, online first:1–18 (2017)

  16. Liu, A., Wang, W., Shang, S., Li, Q., Zhang, X.: Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. online first:1–26 (2017)

  17. Liu, J., Shang, S., Zheng, K., Wen, J.: Multi-view ensemble learning for dementia diagnosis from neuroimaging: An artificial neural network approach. Neurocomputing 195, 112–116 (2016)

    Article  Google Scholar 

  18. Liu, J., Zhao, K., Sommer, P., Shang, S., Kusy, B., Lee, J., Jurdak, R.: A novel framework for online amnesic trajectory compression in resource-constrained environments. IEEE Trans. Knowl. Data Eng. 28(11), 2827–2841 (2016)

    Article  Google Scholar 

  19. Liu, K., Li, Y., Ding, Z., Shang, S., Zheng, K.: Benchmarking big data for trip recommendation. In: ICCCN, pp. 1–6 (2014)

  20. Liu, K., Yang, B., Shang, S., Li, Y., Ding, Z.: MOIR/UOTS: trip recommendation with user oriented trajectory search. In: MDM, pp. 335–337 (2013)

  21. Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group nearest neighbor queries. In: ICDE, pp. 301–312 (2004)

  22. Papadias, D., Tao, Y., Mouratidis, K., Hui, C. K.: Aggregate nearest neighbor queries in spatial databases. TODS 30(2), 529–576 (2005)

    Article  Google Scholar 

  23. Rong, X., Chen, Z., Mei, Q., Egoset, E. Adar.: Exploiting word ego-networks and user-generated ontology for multifaceted set expansion. In: WSDM, pp. 645–654 (2016)

  24. 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 

  25. Shang, S., Chen, L., Wei, Z., Jensen, C. S., Wen, J., Kalnis, P.: Collective travel planning in spatial networks. IEEE Trans. Knowl. Data Eng. 28(5), 1132–1146 (2016)

    Article  Google Scholar 

  26. Shang, S., Chen, L., Wei, Z., Jensen, C. S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. PVLDB 10(11), 1178–1189 (2017)

    Google Scholar 

  27. Shang, S., Deng, K., Xie, K.: Best point detour query in road networks. In: ACM GIS, pp. 71–80 (2010)

  28. Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: EDBT, pp. 156–167 (2012)

  29. 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 

  30. Shang, S., Guo, D., Liu, J., Liu, K.: Human mobility prediction and unobstructed route planning in public transport networks. In: MDM(2), pp. 43–48 (2014)

  31. Shang, S., Guo, D., Liu, J., Zheng, K., Wen, J.: Finding regions of interest using location based social media. Neurocomputing 173, 118–123 (2016)

    Article  Google Scholar 

  32. Shang, S., Liu, J., Zheng, K., Lu, H., Pedersen, T. B., Wen, J.: Planning unobstructed paths in traffic-aware spatial networks. GeoInformatica 19(4), 723–746 (2015)

    Article  Google Scholar 

  33. Shang, S., Lu, H., Pedersen, T. B., Xie, X.: Finding traffic-aware fastest paths in spatial networks. In: SSTD, pp. 128–145 (2013)

  34. Shang, S., Lu, H., Pedersen, T. B., Xie, X.: Modeling of traffic-aware travel time in spatial networks. In: MDM (1), pp. 247–250 (2013)

  35. Shang, S., Yuan, B., Deng, K., Xie, K., Zheng, K., Zhou, X.: Pnn query processing on compressed trajectories. GeoInformatica 16(3), 467–496 (2012)

    Article  Google Scholar 

  36. Shang, S., Yuan, B., Deng, K., Xie, K., Zhou, X.: Finding the most accessible locations: reverse path nearest neighbor query in road networks. In: ACM SIGSPATIAL, pp. 181–190 (2011)

  37. Shang, S., Zheng, K., Jensen, C. S., Yang, B., Kalnis, P., Li, G., Wen, J.: Discovery of path nearby clusters in spatial networks. IEEE Trans. Knowl. Data Eng. 27(6), 1505–1518 (2015)

    Article  Google Scholar 

  38. Shang, S., Zhu, S., Guo, D., Lu, M.: Discovery of probabilistic nearest neighbors in traffic-aware spatial networks. World Wide Web 20(5), 1135–1151 (2017)

    Article  Google Scholar 

  39. Xie, K., Deng, K., Shang, S., Zhou, X., Zheng, K.: Finding alternative shortest paths in spatial networks. ACM Trans. Database Syst. 37(4), 29:1–29:31 (2012)

    Article  Google Scholar 

  40. Xie, Q., Shang, S., Yuan, B., Pang, C., Zhang, X.: Local correlation detection with linearity enhancement in streaming data. In: CIKM, pp. 309–318 (2013)

  41. Xie, X., Lu, H., Chen, J., Shang, S.: Top-k neighborhood dominating query. In: DASFAA, pp. 131–145 (2013)

  42. Xu, Y., Chen, L., Yao, B., Shang, S., Zhu, S., Zheng, K., Li, F.: Location-based top-k term querying over sliding window. In: WISE, pp. 299–314 (2017)

  43. Yang, B., Dai, J., Guo, C., Jensen, C.S.: Pace: A PAth-CEntric paradigm for stochastic path finding. VLDB Journal, online first (2015)

  44. Yang, B., Guo, C., Jensen, C. S., Kaul, M., Shang, S.: Stochastic skyline route planning under time-varying uncertainty. In: ICDE, pp. 136–147 (2014)

  45. Zheng, K., Shang, S., Yuan, N. J., Yang, Y.: Towards efficient search for activity trajectories. In: ICDE, pp. 230–241 (2013)

  46. Zheng, K., Su, H., Zheng, B., Shang, S., Xu, J., Liu, J., Zhou, X.: Interactive top-k spatial keyword queries. In: ICDE, pp. 423–434 (2015)

  47. Zheng, K., Zheng, Y., Yuan, N. J., Shang, S.: On discovery of gathering patterns from trajectories. In: ICDE, pp. 242–253 (2013)

  48. Zheng, K., Zheng, Y., Yuan, N. J., Shang, S., Zhou, X.: Online discovery of gathering patterns over trajectories. IEEE Trans. Knowl. Data Eng. 26(8), 1974–1988 (2014)

    Article  Google Scholar 

  49. Zhu, S., Wang, Y., Shang, S., Zhao, G., Wang, J.: Probabilistic routing using multimodal data. Neurocomputing 253, 49–55 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

This paper is partly supported by Natural Science Foundation of P.R.China (No. 61373147, and No. 61672442), Fujian Province Science and Technology Plan Project (No. 2016Y0079, No. 2017J01783), the Education and Scientific Research Project for Youth and Middle-aged Teachers in Fujian (No. JA15365), the Open Research Fund Program of Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University, and the Open Research Fund Program of Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University.

Author information

Authors and Affiliations

  1. Xiamen University of Technology, Xiamen, China

    Yan Wang, Jianmin Li, Ying Zhong & Shunzhi Zhu

  2. Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University, Shenzhen, China

    Danhuai Guo

  3. Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University CNIC, Chinese Academy of Sciences, Beijing, China

    Danhuai Guo

  4. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Shuo Shang

Authors
  1. Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianmin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shunzhi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Danhuai Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shuo Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shunzhi Zhu or Shuo Shang.

Additional information

This article belongs to the Topical Collection: Special Issue on Geo-Social Computing

Guest Editors: Guandong Xu, Wen-Chih Peng, Hongzhi Yin, Zi (Helen) Huang

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Li, J., Zhong, Y. et al. Discovery of accessible locations using region-based geo-social data. World Wide Web 22, 929–944 (2019). https://doi.org/10.1007/s11280-018-0538-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-018-0538-5

Keywords

Search

Navigation