Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing

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.Comment: 15 page

Specialty-Aware Task Assignment in Spatial Crowdsourcing

“…[4,5] recommend top-k teams with the minimum cost to a specialty-aware task. [3] studies assigning workers for specialty-aware tasks to maximize the total utility score. The difference between our work and [3] is that in our work workers specify fees for each of their skills, and in [3] workers only have a united fee, which is not practical.…”

In-route task selection in crowdsourcing

“…The goal is to maximize the number of assignments and subsequently minimize the total amount of money spent by the requesters, assuming that the price of tasks is attributed by workers. The Multi-Skill Spatial Crowdsourcing (MS-SC) problem is presented in [4]. It aims at assigning multiskilled workers to complex spatial tasks such that skills between workers and tasks match with each other, and workers benefits are maximized.…”

Prediction-Based Task Assignment in Spatial Crowdsourcing

“…(2) obtain a list, L, of valid worker-and-task pairs forwi ∈ W andtj ∈ T (3) for k = 1 to min{|W |, |T |} (4) Sp = ∅; (5) for each valid assignment pair wi,tj ∈ L (6) if wi,tj has lbc ij greater than the remaining budget, then continue; (7) if pair wi,tj cannot be pruned w.r.t. Sp by Lemma 4.1 (8) if pair wi,tj cannot be pruned w.r.t. Sp by Lemma 4.2 (9) add wi,tj to Sp (10) prune other candidate pairs in Sp with wi,tj (11) select one best assignment pair wi,tj in Sp satisfying the budget constraint Bmax in Eq.…”

“…Procedure MQA Decomposition { Input: n current/future workerswi in W , m current/future spatial taskstj in T , and the number of subproblems g Output: the decomposed MQA subproblems, Ms (for 1 ≤ s ≤ g) (1) for s = 1 to g (2) Ms = ∅ (3) compute all valid worker-and-task pairs wi,tj from W and T (4) for s = 1 to g (5) add an anchor tasktj and find its ( m /g − 1) nearest tasks to set T (s) p // the task,tj , whose longitude (or mean of the longitude) is the smallest (6) for each current/future tasktj ∈ T (s) p (7) obtain all valid workerswi that can reach tasktj (8) add these pairs wi,tj to subproblem Ms (9) return subproblems M1, M2, ..., and Mg} Next, we use an example to illustrate how to resolve the conflicts between two (or multiple) pairs w i ,t j and w i ,t b (w.r.t. the conflicting workerw i ), by selecting one "best" pair with low traveling cost and high quality score.…”