Enabling Community-Driven Information Integration Through Clustering
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 40100671
- Type
- D - Journal article
- DOI
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10.1007/s10619-014-7160-z
- Title of journal
- Distributed and Parallel Databases
- Article number
- -
- First page
- 33
- Volume
- 33
- Issue
- 1
- ISSN
- 0926-8782
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
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A - Computer Science
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Crowdsourcing is a hot topic in data quality and integration. This paper is significant because it addresses how to model the fact that crowd workers have different preferences or areas of expertise/interest - in previous work different perspectives were treated as noise.
Keynote at 42nd International Conference on Current Trends in Theory and Practice of Computer Science, Czech Republic 2016.
Enabled funding includes EPSRC Programme Grant (EP/M025268/1) VADA: Value Added Data Systems - Principles and Architecture (GBP4,600,000)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
