个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:计算机科学与技术学院
办公地点:大连理工大学创新园大厦8-A0824
联系方式:18641168567
电子邮箱:gztan@dlut.edu.cn
Utility-Driven Approximate Task Allocation for Crowdsourcing
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论文类型:会议论文
发表时间:2017-01-01
收录刊物:EI、CPCI-S
页面范围:23-30
摘要:Many applications supported by the crowdsourcing are subject to delay constraints. The timely outcome returned with a better partial fulfillment is preferable to the full completion with delay latency. In this paper, we investigate the approximation algorithms for optimal task assignment with partial fulfillment. The objective is to maximize the total utility gain of all the tasks with delay constraints, where the gain depends on the partial values according to the achievement quality. We propose a linear programming problem with equality constraint, and we prove the objective function value of this problem is an upper bound of that of optimal task assignment. A linear programming based approximate algorithm for task assignment with delay constraints is proposed. The correctness of the algorithm is proved. The approximation ratio is provided. Further the problem of optimal task assignment with relaxed constrains is proposed and a lagrange multiplier based approximation algorithm with O{max(i is an element of{1,...,n}){r(i)}n) is given. Evaluation results show that our algorithms have better performance in utility gain.