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Oriented Online Route Recommendation for Spatial Crowdsourcing Task Workers

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Advances in Spatial and Temporal Databases (SSTD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9239))

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Abstract

Emerging spatial crowdsourcing platforms enable the workers (i.e., crowd) to complete spatial crowdsourcing tasks (like taking photos, conducting citizen journalism) that are associated with rewards and tagged with both time and location features. In this paper, we study the problem of online recommending an optimal route for a crowdsourcing worker, such that he can (i) reach his destination on time and (ii) receive the maximum reward from tasks along the route. We show that no optimal online algorithm exists in this problem. Therefore, we propose several heuristics, and powerful pruning rules to speed up our methods. Experimental results on real datasets show that our proposed heuristics are very efficient, and return routes that contain 82–91 % of the optimal reward.

The research is partly supported by grant GRF 152201/14E from Hong Kong RGC.

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Notes

  1. 1.

    www.clickworker.com/en/mobile-crowdsourcing.

  2. 2.

    features.en.softonic.com/mobile-crowdsourcing-does-it-work.

  3. 3.

    www.clickworker.com/en/clickworkerjob, www.lionbridge.com.

  4. 4.

    The reward of a task can be collected by the same worker for only once. Similar to [10], we assume that each task has a unit reward and can be completed immediately.

  5. 5.

    Our method can be applied to any distance function provided that it satisfies the triangle inequality, such as Euclidean distance, Manhattan distance, and road network distance.

  6. 6.

    Available tasks are tasks released before the current time \(t_{now}\).

  7. 7.

    https://foursquare.com/.

  8. 8.

    For each location with only one check-in item (say, at time t), we choose its deadline randomly in \([t, t^+_q]\), where \(t_q^+\) refers to the query’s deadline.

  9. 9.

    As mentioned in Definition 4, \(R_{opt}(q)\) is obtained with assumption that all tasks’ information are known in advance at time \(t_q^-\). With this assumption, OnlineRR becomes a special case of SnapshotRR where tasks can have release time larger than \(t_q^-\) and the approach for SnapshotRR can be used to find \(R_{opt}(q)\) then.

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Authors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

    Yu Li, Man Lung Yiu & Wenjian Xu

Authors
  1. Yu Li
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  2. Man Lung Yiu
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  3. Wenjian Xu
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Corresponding author

Correspondence to Yu Li .

Editor information

Editors and Affiliations

  1. Naval Academy Research Institute, Brest naval, France

    Christophe Claramunt

  2. University of Florida, Gainesville, Florida, USA

    Markus Schneider

  3. Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Raymond Chi-Wing Wong

  4. Emory University, Atlanta, USA

    Li Xiong

  5. Gachon University, Gyeonggi-do, Korea (Republic of)

    Woong-Kee Loh

  6. University of Southern California, Los Angeles, USA

    Cyrus Shahabi

  7. Department of Computer Science, Pusan National University, Pusan, Korea (Republic of)

    Ki-Joune Li

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Li, Y., Yiu, M.L., Xu, W. (2015). Oriented Online Route Recommendation for Spatial Crowdsourcing Task Workers. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-22363-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22362-9

  • Online ISBN: 978-3-319-22363-6

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