# Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing

arXiv: https://arxiv.org/abs/1510.03149 · HTML: https://mlsanity.com/paper/1510.03149/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing [technical report]](https://mlsanity.com/paper/2107.08662/) (2021)
- [Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing](https://mlsanity.com/paper/2410.15449/) (2024)
- [CTG-KrEW: Generating Synthetic Structured Contextually Correlated Content by Conditional Tabular GAN with K-Means Clustering and Efficient Word Embedding](https://mlsanity.com/paper/2409.01628/) (2024)

## Abstract (arXiv)

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.