Skip to main content
SpringerLink
Log in

Market-Driven Optimal Task Assignment in Spatial Crowdsouring

  • Conference paper
  • First Online:
Web-Age Information Management (WAIM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9998))

Included in the following conference series:

Abstract

With the popularity of mobile devices and Online To Offline (O2O) marketing model, various spatial crowdsourcing platforms, such as Gigwalk, WeGoLook, TaskRabbit and gMission, are getting popular. An important task of spatial crowdsourcing platforms is to allocate spatial tasks to suitable workers. Existing approaches only simply focus on maximizing the number of completed spatial tasks but neglect the influence of supplies and demands from real crowdsourcing market, which leads to different optimal objectives for crowdsourcing task assignments. In this paper, to address the shortcomings of the existing approaches, we first propose a more general spatial crowdsourcing task assignment problem, called Market-driven Optimal Task Assignment (MOTA) problem, consisting of two real scenarios, Excess Demand of Crowd Workers and Insufficient Supply of Spatial Tasks, in daily life. Unfortunately, we prove that the two variants of this problem are NP-Hard. Thus, we design two approximation algorithm to solve this problem. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on synthetic datasets.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alt, F., Shirazi, A.S., Schmidt, A., Kramer, U., Nawaz, Z.: Location-based crowdsourcing: extending crowdsourcing to the real world. In: NordiCHI 2010, pp. 13–22 (2010)

    Google Scholar 

  2. Arora, S., Karakostas, G.: A 2+epsilon approximation algorithm for the k-mst problem. In: SODA 2000, pp. 754–759 (2000)

    Google Scholar 

  3. Cao, C.C., She, J., Tong, Y., Chen, L.: Whom to ask?: jury selection for decision making tasks on micro-blog services. Proc. VLDB Endow. 5(11), 1495–1506 (2012)

    Article  Google Scholar 

  4. Cao, C.C., Tong, Y., Chen, L., Jagadish, H.V.: Wisemarket: a new paradigm for managing wisdom of online social users. In: SIGKDD 2013, pp. 455–463 (2013)

    Google Scholar 

  5. Chekuri, C., Kumar, A.: Maximum coverage problem with group budget constraints and applications. In: Jansen, K., Khanna, S., Rolim, J.D.P., Ron, D. (eds.) RANDOM 2004 and APPROX 2004. LNCS, vol. 3122, pp. 72–83. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Deng, D., Shahabi, C., Demiryurek, U.: Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing. In: GIS 2013, pp. 324–333 (2013)

    Google Scholar 

  7. Feige, U.: A threshold of \(\ln n\) for approximating set cover. J. ACM 45(4), 634–652 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  8. Gao, D., Tong, Y., She, J., Song, T., Chen, L., Xu, K.: Top-k team recommendation in spatial crowdsourcing. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds.) WAIM 2016. LNCS, vol. 9658, pp. 191–204. Springer, Heidelberg (2016). doi:10.1007/978-3-319-39937-9_15

    Chapter  Google Scholar 

  9. Karp, R.M., Vazirani, U.V., Vazirani, V.V.: An optimal algorithm for on-line bipartite matching. In: STOC 1990 (1990)

    Google Scholar 

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

    Google Scholar 

  11. Khuller, S., Moss, A., Naor, J.: The budgeted maximum coverage problem. Inf. Process. Lett. 70(1), 39–45 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  12. Long, C., Wong, R.C.W., Yu, P.S., Jiang, M.: On optimal worst-case matching. In: SIGMOD 2013, pp. 845–856 (2013)

    Google Scholar 

  13. Pournajaf, L., Xiong, L., Sunderam, V.S., Goryczka, S.: Spatial task assignment for crowd sensing with cloaked locations. In: MDM 2014, pp. 189–198 (2014)

    Google Scholar 

  14. She, J., Tong, Y., Chen, L.: Utility-aware social event-participant planning. In: SIGMOD 2015, pp. 1629–1643 (2015)

    Google Scholar 

  15. She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement. In: ICDE 2015, pp. 735–746 (2015)

    Google Scholar 

  16. She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement and its variant for online setting. IEEE Trans. Knowl. Data Eng. 28(9), 2281–2295 (2016)

    Article  Google Scholar 

  17. Tong, Y., Cao, C.C., Chen, L.: TCS: efficient topic discovery over crowd-oriented service data. In: SIGKDD 2014, pp. 861–870 (2014)

    Google Scholar 

  18. Tong, Y., Cao, C.C., Zhang, C.J., Li, Y., Chen, L.: Crowdcleaner: data cleaning for multi-version data on the web via crowdsourcing. In: ICDE 2014, pp. 1182–1185 (2014)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  21. Tong, Y., She, J., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web J. 19(6), 1151–1177 (2016)

    Article  Google Scholar 

  22. Tsitsiklis, J.N.: Special cases of traveling salesman and repairman problems with time windows. Networks 22(3), 263–282 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  23. Yiu, M.L., Mouratidis, K., Mamoulis, N.: Capacity constrained assignment in spatial databases. In: SIGMOD 2008, pp. 15–28 (2008)

    Google Scholar 

  24. Wong, R.C.W., Tao, Y., Fu, A.W.C., Xiao, X.: On efficient spatial matching. In: VLDB 2007, pp. 579–590 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

    Kaitian Tan & Qian Tao

Authors
  1. Kaitian Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qian Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaitian Tan .

Editor information

Editors and Affiliations

  1. Tsinghua University , Beijing, China

    Shaoxu Song

  2. Beihang University , Beijing, China

    Yongxin Tong

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Tan, K., Tao, Q. (2016). Market-Driven Optimal Task Assignment in Spatial Crowdsouring. In: Song, S., Tong, Y. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9998. Springer, Cham. https://doi.org/10.1007/978-3-319-47121-1_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47121-1_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47120-4

  • Online ISBN: 978-3-319-47121-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation