{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:06:05Z","timestamp":1787022365241,"version":"build-2736575974"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Recently, with the rapid development of mobile devices and the crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions, and workers need to physically move to specified locations to conduct tasks. Many works have studied task assignment problems in spatial crowdsourcing, however, their problem settings are different from each other. Thus, it is hard to compare the performances of existing algorithms on task assignment in spatial crowdsourcing. In this paper, we present a comprehensive experimental comparison of most existing algorithms on task assignment in spatial crowdsourcing. Specifically, we first give general definitions about spatial workers and spatial tasks based on definitions in the existing works such that the existing algorithms can be applied on the same synthetic and real data sets. Then, we provide a uniform implementation for all the tested algorithms of task assignment problems in spatial crowdsourcing (open sourced). Finally, based on the results on both synthetic and real data sets, we discuss the strengths and weaknesses of tested algorithms, which can guide future research on the same area and practical implementations of spatial crowdsourcing systems.<\/jats:p>","DOI":"10.14778\/3236187.3236196","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T08:12:28Z","timestamp":1536567148000},"page":"1428-1440","source":"Crossref","is-referenced-by-count":18,"title":["An experimental evaluation of task assignment in spatial crowdsourcing"],"prefix":"10.14778","volume":"11","author":[{"given":"Peng","family":"Cheng","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Jian","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"https:\/\/github.com\/gmission\/SpacialCrowdsourcing","year":"2018","unstructured":"{online} spatial crowdsourcing benchmark algorithms. https:\/\/github.com\/gmission\/SpacialCrowdsourcing , 2018 . {online} spatial crowdsourcing benchmark algorithms. https:\/\/github.com\/gmission\/SpacialCrowdsourcing, 2018."},{"key":"e_1_2_1_2_1","volume-title":"https:\/\/github.com\/gmission\/SCDataGenerator","year":"2018","unstructured":"{online} spatial crowdsourcing dataset generator. https:\/\/github.com\/gmission\/SCDataGenerator , 2018 . {online} spatial crowdsourcing dataset generator. https:\/\/github.com\/gmission\/SCDataGenerator, 2018."},{"key":"e_1_2_1_3_1","volume-title":"https:\/\/www.didichuxing.com","author":"Chuxing DiDi","year":"2018","unstructured":"{online} DiDi Chuxing . https:\/\/www.didichuxing.com , 2018 . {online} DiDi Chuxing. https:\/\/www.didichuxing.com, 2018."},{"key":"e_1_2_1_4_1","volume-title":"https:\/\/www.fieldagent.net","author":"Agent Field","year":"2018","unstructured":"{online} Field Agent . https:\/\/www.fieldagent.net , 2018 . {online} Field Agent. https:\/\/www.fieldagent.net, 2018."},{"key":"e_1_2_1_5_1","volume-title":"http:\/\/gmission.github.io","year":"2018","unstructured":"{online} gMission. http:\/\/gmission.github.io , 2018 . {online} gMission. http:\/\/gmission.github.io, 2018."},{"key":"e_1_2_1_6_1","volume-title":"https:\/\/www.google.com\/maps\/views\/streetview","author":"Street View GoogleMap","year":"2018","unstructured":"{online} GoogleMap Street View . https:\/\/www.google.com\/maps\/views\/streetview , 2018 . {online} GoogleMap Street View. https:\/\/www.google.com\/maps\/views\/streetview, 2018."},{"key":"e_1_2_1_7_1","volume-title":"http:\/\/crowdflower.com\/","year":"2018","unstructured":"{online} TaskRabbit. http:\/\/crowdflower.com\/ , 2018 . {online} TaskRabbit. http:\/\/crowdflower.com\/, 2018."},{"key":"e_1_2_1_8_1","volume-title":"https:\/\/www.uber.com","year":"2018","unstructured":"{online} uber. https:\/\/www.uber.com , 2018 . {online} uber. https:\/\/www.uber.com, 2018."},{"key":"e_1_2_1_9_1","volume-title":"DTIC Document","author":"Ahuja R. K.","year":"1988","unstructured":"R. K. Ahuja , T. L. Magnanti , and J. B. Orlin . Network flows. Technical report , DTIC Document , 1988 . R. K. Ahuja, T. L. Magnanti, and J. B. Orlin. Network flows. Technical report, DTIC Document, 1988."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733004.2733047"},{"key":"e_1_2_1_11_1","unstructured":"P. Cheng X. Jian and L. Chen. Task assignment on spatial crowdsourcing (technical report). http:\/\/arxiv.org\/abs\/1605.09675.  P. Cheng X. Jian and L. Chen. Task assignment on spatial crowdsourcing (technical report). http:\/\/arxiv.org\/abs\/1605.09675."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2550041"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/2794367.2794372"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864349.1864380"},{"key":"e_1_2_1_15_1","first-page":"20","volume-title":"Maximum likelihood estimation of observer error-rates using the em algorithm. Applied statistics","author":"Dawid A. P.","year":"1979","unstructured":"A. P. Dawid and A. M. Skene . Maximum likelihood estimation of observer error-rates using the em algorithm. Applied statistics , pages 20 -- 28 , 1979 . A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the em algorithm. Applied statistics, pages 20--28, 1979."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2525314.2525370"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10707-016-0251-4"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/UIC-ATC-ScalCom.2014.68"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498229"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1837885.1837906"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2424321.2424346"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2525314.2525346"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2557642.2578223"},{"key":"e_1_2_1_24_1","volume-title":"Pearson Education India","author":"Kleinberg J.","year":"2006","unstructured":"J. Kleinberg and \u00c9. Tardos. Algorithm design . Pearson Education India , 2006 . J. Kleinberg and \u00c9. Tardos. Algorithm design. Pearson Education India, 2006."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22363-6_8"},{"key":"e_1_2_1_26_1","volume-title":"WH Free. Co.","author":"Michael R. G.","year":"1979","unstructured":"R. G. Michael and S. J. David . Computers and intractability: a guide to the theory of np-completeness . WH Free. Co. , San Fr , 1979 . R. G. Michael and S. J. David. Computers and intractability: a guide to the theory of np-completeness. WH Free. Co., San Fr, 1979."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2014.15"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2016.7457121"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2729713"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498228"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.846291"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3236187.3236196","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T04:45:38Z","timestamp":1672202738000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3236187.3236196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":31,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2018,7]]}},"alternative-id":["10.14778\/3236187.3236196"],"URL":"https:\/\/doi.org\/10.14778\/3236187.3236196","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,7]]}}}