Crowdsourced Data Management: Industry and Academic Perspectives

2015 ◽  
Vol 6 (1-2) ◽  
pp. 1-161 ◽  
Author(s):  
Adam Marcus ◽  
Aditya Parameswaran
Keyword(s):  
Data Management ◽  
Crowdsourced Data

Crowdsourced Data Management

2018 ◽  
Author(s):  
Guoliang Li ◽  
Jiannan Wang ◽  
Yudian Zheng ◽  
Ju Fan ◽  
Michael J. Franklin
Keyword(s):  
Data Management ◽  
Crowdsourced Data

Crowdsourced Data Management: A Survey

2017 ◽  
Author(s):  
Guoliang Li ◽  
Jiannan Wang ◽  
Yudian Zheng ◽  
Michael Franklin
Keyword(s):  
Data Management ◽  
Crowdsourced Data

Crowdsourced Data Management: A Survey

2016 ◽  
Vol 28 (9) ◽  
pp. 2296-2319 ◽  
Author(s):  
Guoliang Li ◽  
Jiannan Wang ◽  
Yudian Zheng ◽  
Michael J. Franklin
Keyword(s):  
Data Management ◽  
Crowdsourced Data

Crowdsourced Data Management

2017 ◽  
Author(s):  
Guoliang Li ◽  
Yudian Zheng ◽  
Ju Fan ◽  
Jiannan Wang ◽  
Reynold Cheng
Keyword(s):  
Data Management ◽  
Crowdsourced Data

scholarly journals Intensional data on the web

2017 ◽  
Author(s):  
Antoine Amarilli ◽  
Silviu Maniu ◽  
Pierre Senellart
Keyword(s):  
Social Networks ◽  
Data Management ◽  
Data Access ◽  
Influence Maximization ◽  
Management Scenarios ◽  
Focused Crawling ◽  
Crowdsourced Data ◽  
The World ◽  
Web Data Management ◽  
The Web

We call data intensional when it is not directly available, but must be accessed through a costlyinterface. Intensional data naturally arises in a number of Web data management scenarios, suchas Web crawling or ontology-based data access. Such scenarios require us to model an uncertainview of the world, for which, given a query, we must answer the question “What is the best thingto do next?” Once data has been retrieved, the knowledge of the world is revised, and the wholeprocess is repeated, until enough knowledge about the world has been obtained for the particularapplication considered. In this article, we give an overview of the steps underlying all intensionaldata management scenarios, and illustrate them on three concrete applications: focused crawling,online influence maximization in social networks, and mining crowdsourced data.

scholarly journals CyLog/Game aspect: An approach to separation of concerns in crowdsourced data management

2016 ◽  
Vol 62 ◽  
pp. 170-184 ◽  
Author(s):  
Atsuyuki Morishima ◽  
Shun Fukusumi ◽  
Hiroyuki Kitagawa
Keyword(s):  
Data Management ◽  
Separation Of Concerns ◽  
Crowdsourced Data
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