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Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing
Databases · Submitted 12 Oct 2015
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.
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The abstract, sentence by sentence
With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers' benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy, g-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. | background 0.91 | 0.16 | 0.03 | asserted 0.97 |
| s3 | Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers' benefits are maximized under the budget constraint. | contribution 0.89 | 0.57 | 0.18 | asserted 0.95 |
| s4 | We prove that the MS-SC problem is NP-hard and intractable. | result 0.65 | 0.92 | 0.15 | asserted 1.00 |
| s5 | Therefore, we propose three effective heuristic approaches, including greedy, g-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. | method 0.63 | 0.93 | 0.16 | asserted 1.00 |
| s6 | Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets. | result 0.94 | 0.97 | 0.48 | asserted 1.00 |
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Original abstract
With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers' benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy, g-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.
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