{"title":"实时空间众包多工作者任务的离线工作者选择","authors":"Yongjian Zhao, Qi Han","doi":"10.1109/MDM.2019.00117","DOIUrl":null,"url":null,"abstract":"Spatial crowdsourcing consists of location-specific tasks that require people to be physically at specific locations to complete them. In this paper we focus on worker selection for spatial crowdsourcing where each task requires multiple workers to accomplish. We mathematically formulate the problem and prove its APX-hardness. We develop efficient greedy algorithms with a good approximation ratio. Compared with state-of-the art approach, our proposed algorithm outperforms by 35%.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Offline Worker Selection for Real-Time Spatial Crowdsourcing Multi-Worker Tasks\",\"authors\":\"Yongjian Zhao, Qi Han\",\"doi\":\"10.1109/MDM.2019.00117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spatial crowdsourcing consists of location-specific tasks that require people to be physically at specific locations to complete them. In this paper we focus on worker selection for spatial crowdsourcing where each task requires multiple workers to accomplish. We mathematically formulate the problem and prove its APX-hardness. We develop efficient greedy algorithms with a good approximation ratio. Compared with state-of-the art approach, our proposed algorithm outperforms by 35%.\",\"PeriodicalId\":241426,\"journal\":{\"name\":\"2019 20th IEEE International Conference on Mobile Data Management (MDM)\",\"volume\":\"205 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 20th IEEE International Conference on Mobile Data Management (MDM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MDM.2019.00117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDM.2019.00117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Offline Worker Selection for Real-Time Spatial Crowdsourcing Multi-Worker Tasks
Spatial crowdsourcing consists of location-specific tasks that require people to be physically at specific locations to complete them. In this paper we focus on worker selection for spatial crowdsourcing where each task requires multiple workers to accomplish. We mathematically formulate the problem and prove its APX-hardness. We develop efficient greedy algorithms with a good approximation ratio. Compared with state-of-the art approach, our proposed algorithm outperforms by 35%.