| EPSRC Reference: |
EP/N011589/1 |
| Title: |
BetterCrowd: Human Computation for Big Data |
| Principal Investigator: |
Demartini, Dr G |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Information School |
| Organisation: |
University of Sheffield |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
01 January 2016 |
Ends: |
30 June 2017 |
Value (£): |
99,555
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Human-Computer Interactions |
| Information & Knowledge Mgmt |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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15 Jul 2015
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EPSRC ICT Prioritisation Panel - Jul 2015
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Announced
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Summary on Grant Application Form |
In the last few years we have seen a rapid increase of available data. Digitization has become endemic. This has lead to a data deluge that left many unable to cope with such large amounts of messy data. Also because of the large number of content producers and different formats, data is not always easy to process by machines due to its its diverse quality and the presence of bias. Thus, in the current data-driven economy, if organizations can effectively analyze data at scale and use it as decision-support infrastructure at the executive level, data will lead to a key competitive advantage. To deal with the current data deluge, in the BetterCrowd project I will define and evaluate Human Computation methods to improve both the effectiveness and efficiency of currently available hybrid Human-Machine systems.
Human Computation (HC) is a game-changing paradigm that systematically exploits human intelligence at scale to improve purely machine-based data management systems (see, for example, CrowdDB [13]). This is often obtained by means of Crowdsourcing, that is, outsourcing certain tasks from the machine to a crowd of human individuals who perform short tasks (also known as Human Intelligence Tasks or HITs) that are simple for humans but still difficult for machines (e.g., understanding the content of a picture or sarcasm in text). Involving humans in the computation process is a fundamental scientific challenge that requires obtaining the best from human abilities and effectively embedding them into traditional computational systems. The challenges involved with the use of HC are both its efficiency (i.e., humans are naturally slower than machines in terms of information processing) and effectiveness (i.e., while machines deterministically compute, humans behavior may be unpredictable and possibly malicious).
The project is composed of two main parts. We will first look at how to improve crowdsourcing effectiveness by proposing novel techniques to detect malicious workers in crowdsourcing platforms. In the second part, we will make HC techniques scale so that they can be applied to larger volume of data focusing on scheduling tasks to the crowd (WP2).
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.shef.ac.uk |