Collaboratively crowdsourcing workflows with turkomatic
By Anand Pramod Kulkarni, Matthew Can, Bjoern Hartmann. Conference on Computer Supported Cooperative Work, 2012
323 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/d4e5e2825eae83c543628b72905bee4db2888a26
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Michael S. Bernstein
- Daniel S. Weld
- Adam Marcus
- Aditya G. Parameswaran
- Lydia B. Chilton
- Joy Kim
- Sarah Sterman
- Allegra Argent Beal Cohen
In Computer Science Applications
- Michael S. Bernstein
- Adam Marcus
- Aditya G. Parameswaran
- Lydia B. Chilton
- Jaime Teevan
- Shamsi T. Iqbal
- Daniel S. Weld
- Ece Kamar
In Management Science and Operations Research
- Adam Marcus
- Aditya G. Parameswaran
- Hector Garcia-Molina
- Jaime Teevan
- Michael S. Bernstein
- Shamsi T. Iqbal
- Daniel S. Weld
- Ece Kamar
Citations in the corpus
- Mechanical Novel: Crowdsourcing Complex Work through Reflection and Revision (2016, methodology): “These microtask systems present work in an assembly line-like structure called a workflow [6], using mechanisms such as iteration [25], clustering [9], voting [26], and other patterns for splitting work [5, 19, 21].”
- Cascade: crowdsourcing taxonomy creation (2013, background): “Turk-o-matic [12] asks workers to break down the task and then creates subtasks for more workers to do.”
- Crowdsourcing in Computer Vision (2016, unknown): “…systems include TurKit [Little et al., 2010]2, CLOWDER that uses decision-theoretic optimization to dynamically control the workflow [Weld et al., 2011], Turkomatic [Kulkarni et al., 2012], a cloud service tool from [Matera et al., 2014], and a recent light-weight task management system SimpleAMT3.”
- Context Trees: Crowdsourcing Global Understanding from Local Views (2014, background): “If the task is iterative, workers can leave an annotated history for later workers (Lasecki et al. 2012b; 2013), but this again only succeeds if the history is compact enough to be consumed by the next worker.”
- Challenges in Data Crowdsourcing (2016, background): “This human-human region in the space, is still active in modern days: systems like Turkomatic [29], Crowd- Forge [28], and Flash Teams [42], involve humans in the design of the tasks to be solved by human workers.”
- Communicating Context to the Crowd for Complex Writing Tasks (2017, background): “For instance, Mobi enables workers to view the current state of global constraints while performing tasks [17, 28], and Turkomatic [20] asks workers to take responsibility in dividing tasks into subtasks, a process that had previously been done by the requester or through automatic workflows (e.”
- Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “This research proposes multistage workflows to break work into manageable subtasks [119], maintain global constraints across multiple tasks [200], iteratively refine previous workers’ efforts [126], or assemble teams of expert workers to collaborate on difficult or creative tasks [159].”