Skip to main content
SpringerLink
Log in

Multi-skill aware task assignment in real-time spatial crowdsourcing

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

With the development of mobile Internet and the prevalence of sharing economy, spatial crowdsourcing (SC) is becoming more and more popular and attracts attention from both academia and industry. A fundamental issue in SC is assigning tasks to suitable workers to obtain different global objectives. Existing works often assume that the tasks in SC are micro and can be completed by any single worker. However, there also exist macro tasks which need a group of workers with different kinds of skills to complete collaboratively. Although there have been a few works on macro task assignment, they neglect the dynamics of SC and assume that the information of the tasks and workers can be known in advance. This is not practical as in reality tasks and workers appear dynamically and task assignment should be performed in real time according to partial information. In this paper, we study the multi-skill aware task assignment problem in real-time SC, whose offline version is proven to be NP-hard. To solve the problem effectively, we first propose the Online-Exact algorithm, which always computes the optimal assignment for the newly appearing tasks or workers. Because of Online-Exact’s high time complexity which may limit its feasibility in real time, we propose the Online-Greedy algorithm, which iteratively tries to assign workers who can cover more skills with less cost to a task until the task can be completed. We finally demonstrate the effectiveness and efficiency of our solutions via experiments conducted on both synthetic and real datasets.

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

Similar content being viewed by others

References

  1. Liu X, He Q, Tian Y, Lee W, McPherson J, Han J (2012) Event-based social networks: linking the online and offline social worlds. KDD:1032–1040

  2. Liu A, Wang W, Shang S, Li Q, Zhang X (2018) Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica 22 (2):335–362

    Article  Google Scholar 

  3. Kazemi L, Shahabi C (2012) Geocrowd: enabling query answering with spatial crowdsourcing. GIS:189–198

  4. Gao D, Tong Y, She J, Song T, Chen L, Xu K (2017) Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing. Data Sci Eng 2(2):136–150

    Article  Google Scholar 

  5. Xu Y, Chen L, Yao B, Shang S, Zhu S, Zheng K, Li F (2017) Location-based Top-k Term Querying over Sliding Window. WISE:299–314

  6. Anagnostopoulos A, Becchetti L, Castillo C, Gionis A, Leonardi S (2012) Online team formation in social networks. WWW:839–848

  7. Gao D, Tong Y, She J, Song T, Chen L, Xu K (2016) Top-k Team Recommendation in Spatial Crowdsourcing WAIM:191–204

    Chapter  Google Scholar 

  8. Chen L, Shang S, Yao B, Zheng K (2018) Spatio-temporal top-k term search over sliding window. World Wide Web:1–18

  9. Lappas T, Liu K, Terzi E (2009) Finding a team of experts in social networks. KDD:467–476

  10. Majumder A, Datta S, Naidu KVM (2012) Capacitated team formation problem on social networks. KDD:1005–1013

  11. Zhao K, Liu Y, Yuan Q, Chen L, Chen Z, Cong G (2016) Towards Personalized Maps: Mining User Preferences from Geo-textual Data. PVLDB 9(13):1545–1548

    Google Scholar 

  12. Song T, Tong Y, Wang L, She J, Yao B, Chen L, Xu K (2017) Trichromatic online matching in Real-Time spatial crowdsourcing. ICDE:1009–1020

  13. Tao Q, Zeng Y, Zhou Z, Tong Y, Chen L, Xu K (2018) Multi-Worker-Aware Task planning in Real-Time spatial crowdsourcing. DASFAA:301–317

    Chapter  Google Scholar 

  14. Li M, Chen L, Cong G, Gu Y, Yu G (2016) Efficient processing of Location-Aware group preference queries. CIKM:559–568

  15. Zhao K, Chen L, Cong G (2016) Topic exploration in Spatio-Temporal document collections. SIGMOD:985–998

  16. Zeng Y, Tong Y, Chen L, Zhou Z (2018) Latency-Oriented Task completion via spatial crowdsourcing. ICDE:317–328

  17. Tong Y, Wang L, Zhou Z, Chen L, Du B, Ye J (2018) Dynamic pricing in spatial crowdsourcing: a Matching-Based approach. SIGMOD:773–788

  18. Chen L, Cong G, Jensen CS, Wu D (2013) Spatial Keyword Query Processing: An Experimental Evaluation. PVLDB 6(3):217–228

    Google Scholar 

  19. Kargar M, An A (2011) Discovering top-k teams of experts with/without a leader in social networks. CIKM:985–994

  20. Tran L, To H, Fan L, Shahabi C (2018) A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing. TIST 9(3):37:1-37:26

    Article  Google Scholar 

  21. Tong Y, Zhou Z (2018) Dynamic task assignment in spatial crowdsourcing. SIGSPATIAL Special 10(2):18–25

    Article  Google Scholar 

  22. Tong Y, Chen L, Zhou Z, Jagadish HV, Shou L, Lv W (2018) SLADE: A smart Large-Scale task decomposer in crowdsourcing. TKDE 30(8):1588–1601

    Google Scholar 

  23. Song T, Zhu F, Xu K (2108) Specialty-Aware Task assignment in spatial crowdsourcing. AISC:243– 254

    Chapter  Google Scholar 

  24. Tong Y, Chen L, Shahabi C (2017) Spatial crowdsourcing: challenges, Techniques, and Applications. PVLDB 10(12):1988–1991

    Google Scholar 

  25. Vazirani VV (2013) Approximation algorithms. Springer Science & Business Media, Berlin

  26. Tong Y, Wang L, Zhou Z, Ding B, Chen L, Ye J, Xu K (2017) Flexible online task assignment in real-time spatial data. PVLDB 10(11):1334–1345

    Google Scholar 

  27. Tong Y, She J, Ding B, Wang L, Chen L (2016) Online mobile micro-task allocation in spatial crowdsourcing. ICDE:49–60

  28. Cheng P, Lian X, Chen L, Han J, Zhao J (2016) Task assignment on Multi-Skill oriented spatial crowdsourcing. TKDE 28(8):2201–2215

    Google Scholar 

  29. Tong Y, She J, Ding B, Chen L, Wo T, Xu K (2016) Online minimum matching in real-time spatial data: experiments and analysis. PVLDB 9(12):1053–1064

    Google Scholar 

  30. Chen Z, Fu R, Zhao Z, Liu Z, Xia L, Chen L, Cheng P, Cao C, Tong Y, Zhang C (2014) gMission: A General Spatial Crowdsourcing Platform. PVLDB 7(13):1629–1632. http://gmission.github.io

    Google Scholar 

  31. Tong Y, Zeng Y, Zhou Z, Chen L, Ye J, Xu K (2018) A unified approach to route planning for shared mobility. PVLDB 11(11):1633–1646

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SKLSDE Lab and BDBC, School of Computer Science and Engineering and IRI, Beihang University, Beijing, China

    Tianshu Song, Ke Xu, Jiangneng Li, Yiming Li & Yongxin Tong

Authors
  1. Tianshu Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ke Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiangneng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yiming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yongxin Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tianshu Song.

Additional information

Publisher’s note

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

Grants or other notes about the article that should go on the front page should be placed here. General acknowledgments should be placed at the end of the article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, T., Xu, K., Li, J. et al. Multi-skill aware task assignment in real-time spatial crowdsourcing. Geoinformatica 24, 153–173 (2020). https://doi.org/10.1007/s10707-019-00351-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-019-00351-4

Keywords

Search

Navigation