Paper Info
Title
Crowdsourced Data Management: Overview and Challenges.
Abstract
Many important data management and analytics tasks cannot be completely addressed by automated processes. Crowdsourcing is an effective way to harness human cognitive abilities to process these computer-hard tasks, such as entity resolution, sentiment analysis, and image recognition. Crowdsourced data management has been extensively studied in research and industry recently. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowdsourced data management. We first give an overview of crowdsourcing, and then summarize the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data management. Next we review crowdsourced operators, including selection, collection, join, top-k, sort, categorize, aggregation, skyline, planning, schema matching, mining and spatial crowdsourcing. We also discuss crowdsourcing optimization techniques and systems. Finally, we provide the emerging challenges.
Year
DOI
Venue
2017
10.1145/3035918.3054776
SIGMOD Conference
Keywords
Field
DocType
Crowdsourcing,Data Management,Crowdsourcing Optimization
Skyline,Data science,Data mining,Sentiment analysis,Computer science,Crowdsourcing,sort,Analytics,Schema matching,Data management,Crowdsourcing software development,Database
Conference
Citations 
PageRank 
References 
16
0.58
90
Authors
5
Name
Order
Citations
PageRank
Guoliang Li13077154.70
Yudian Zheng241816.91
Ju Fan340628.58
Jiannan Wang4110945.38
Reynold Cheng53069154.13