Skip to main content

Federated Trajectory Search via a Lightweight Similarity Computation Framework

  • Conference paper
  • First Online:
Web and Big Data (APWeb-WAIM 2023)

Abstract

Contact tracing is one of the most effective ways of disease control during a pandemic. A typical method for contact tracing is to examine the spatio-temporal companion between the trajectories of patients and others. However, human trajectory data collected by mobile devices cannot be directly shared due to privacy. To utilize personal trajectory data in contact tracing, this paper presents a federated trajectory search engine called Fetra, which can efficiently process top-k search over a data federation composed of numerous mobile devices without uploading raw trajectories. To achieve this, we first propose a lightweight similarity measure LCTS based on spatio-temporal companion time to evaluate the similarity between trajectories. We then build a federated grid index named FGI via location anonymization. Given a query, a pruning strategy over FGI is applied to prune the candidate mobile devices dynamically. In addition, we propose a local optimization strategy to accelerate similarity computations in mobile devices. Extensive experiments on real-world dataset verify the effectiveness of LCTS and the efficiency of Fetra.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Foursquare dataset (2014). https://www.kaggle.com/datasets/chetanism/foursquare-nyc-and-tokyo-checkin-dataset

  2. Spatio-temporal companion (2021). https://www.bloomberg.com/news/articles/2021-11-08/people-you-don-t-know-and-can-t-see-are-close-contacts-in-china

  3. Amap (2022). https://www.amap.com/

  4. GPS accuracy (2022). https://www.gps.gov/systems/gps/performance/accuracy/

  5. Chen, C., Ding, Y., Wang, Z., Zhao, J., Guo, B., Zhang, D.: VTracer: when online vehicle trajectory compression meets mobile edge computing. IEEE Syst. J. 14(2), 1635–1646 (2020)

    Article  Google Scholar 

  6. Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD, pp. 491–502 (2005)

    Google Scholar 

  7. Chow, C.Y., Mokbel, M.F., Aref, W.G.: Casper*: query processing for location services without compromising privacy. TODS 20, 1–45 (2010)

    Google Scholar 

  8. Christodoulou, G., Bouros, P., Mamoulis, N.: Hint: a hierarchical index for intervals in main memory, pp. 1257–1270, June 2022

    Google Scholar 

  9. Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: VLDB, pp. 426–435 (1997)

    Google Scholar 

  10. Ding, X., Chen, L., Gao, Y., Jensen, C.S., Bao, H.: UlTraMan: a unified platform for big trajectory data management and analytics. PVLDB 11(7), 787–799 (2018)

    Google Scholar 

  11. Hu, Y., et al.: Salon: a universal stay point-based location analysis platform. In: SIGSPATIAL, pp. 407–410, November 2021

    Google Scholar 

  12. Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)

    Article  Google Scholar 

  13. Kato, F., Cao, Y., Yoshikawa, M.: PCT-TEE: trajectory-based private contact tracing system with trusted execution environment. ACM Trans. Spatial Algorithms Syst. 8(2), 1–35 (2022)

    Article  Google Scholar 

  14. Lemire, D., Boytsov, L.: Decoding billions of integers per second through vectorization. Softw. Practice Exp. 45(1), 1–29 (2015)

    Article  Google Scholar 

  15. Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: SIGSPATIAL, pp. 1–10 (2008)

    Google Scholar 

  16. Li, R., et al.: TrajMesa: a distributed NoSQL-based trajectory data management system. IEEE Trans. Knowl. Data Eng. 14(8), 1013–1027 (2021)

    Google Scholar 

  17. Nutanong, S., Jacox, E.H., Samet, H.: An incremental Hausdorff distance calculation algorithm. PVLDB 4(8), 506–517 (2011)

    Google Scholar 

  18. Pérez-Torres, R., Torres-Huitzil, C., Galeana-Zapién, H.: Full on-device stay points detection in smartphones for location-based mobile applications. Sensors 16(10), 1693 (2016)

    Article  Google Scholar 

  19. Schmuck, M., Bazant, M.Z.: Computing the discrete Fréchet distance in subquadratic time. SIAM J. Comput. 75(3), 1369–1401 (2015)

    Google Scholar 

  20. 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 

  21. Shang, Z., Li, G., Bao, Z.: DITA: distributed in-memory trajectory analytics. In: SIGMOD, pp. 725–740 (2018)

    Google Scholar 

  22. Shi, Y., Tong, Y., Zeng, Y., Zhou, Z., Ding, B., Chen, L.: Efficient approximate range aggregation over large-scale spatial data federation. IEEE Trans. Knowl. Data Eng. 35(1), 418–430 (2021)

    Google Scholar 

  23. Su, H., Liu, S., Zheng, B., Zhou, X., Zheng, K.: A survey of trajectory distance measures and performance evaluation. VLDB J. 29, 3–32 (2020)

    Article  Google Scholar 

  24. Tong, Y., et al.: Hu-Fu: efficient and secure spatial queries over data federation. PVLDB 15(6), 1159–1172 (2022)

    Google Scholar 

  25. Vlachos, M., Gunopulos, D., Kollios, G.: Robust similarity measures for mobile object trajectories. In: ICDE, pp. 721–726 (2002)

    Google Scholar 

  26. Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: ICDE, pp. 673–684 (2002)

    Google Scholar 

  27. Wang, H., Li, Y., Gao, C., Wang, G., Tao, X., Jin, D.: Anonymization and de-anonymization of mobility trajectories: dissecting the gaps between theory and practice. IEEE Trans. Mob. Comput. 20(3), 796–815 (2021)

    Article  Google Scholar 

  28. Wang, S., Bao, Z., Culpepper, J.S., Sellis, T., Qin, X.: Fast large-scale trajectory clustering. PVLDB 13(1), 29–42 (2019)

    Google Scholar 

  29. Wang, S., Bao, Z., Culpepper, J.S., Xie, Z., Liu, Q., Qin, X.: Torch: a search engine for trajectory data. In: SIGIR, pp. 535–544 (2018)

    Google Scholar 

  30. Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: WWW, pp. 791–800 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhiyong Peng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, C., Peng, Z. (2024). Federated Trajectory Search via a Lightweight Similarity Computation Framework. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-2390-4_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2389-8

  • Online ISBN: 978-981-97-2390-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics