Provenance network analytics: an approach to data analytics using data provenance
Provenance network analytics is a novel data analytics approach that helps infer properties of data, such as quality or importance, from their provenance. Instead of analysing application data, which are typically domain-dependent, it analyses the data's provenance as represented using the Worl...
| Main Authors: | , , , , |
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| Format: | Article |
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Springer
2018
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| Online Access: | https://eprints.nottingham.ac.uk/48901/ |
| _version_ | 1848797874462130176 |
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| author | Huynh, Trung Dong Ebden, Mark Fischer, Joel E. Roberts, Stephen Moreau, Luc |
| author_facet | Huynh, Trung Dong Ebden, Mark Fischer, Joel E. Roberts, Stephen Moreau, Luc |
| author_sort | Huynh, Trung Dong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Provenance network analytics is a novel data analytics approach that helps infer properties of data, such as quality or importance, from their provenance. Instead of analysing application data, which are typically domain-dependent, it analyses the data's provenance as represented using the World Wide Web Consortium's domain-agnostic PROV data model. Specifically, the approach proposes a number of network metrics for provenance data and applies established machine learning techniques over such metrics to build predictive models for some key properties of data. Applying this method to the provenance of real-world data from three different applications, we show that it can successfully identify the owners of provenance documents, assess the quality of crowdsourced data, and identify instructions from chat messages in an alternate-reality game with high levels of accuracy. By so doing, we demonstrate the different ways the proposed provenance network metrics can be used in analysing data, providing the foundation for provenance-based data analytics. |
| first_indexed | 2025-11-14T20:10:49Z |
| format | Article |
| id | nottingham-48901 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:10:49Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-489012020-05-04T19:32:31Z https://eprints.nottingham.ac.uk/48901/ Provenance network analytics: an approach to data analytics using data provenance Huynh, Trung Dong Ebden, Mark Fischer, Joel E. Roberts, Stephen Moreau, Luc Provenance network analytics is a novel data analytics approach that helps infer properties of data, such as quality or importance, from their provenance. Instead of analysing application data, which are typically domain-dependent, it analyses the data's provenance as represented using the World Wide Web Consortium's domain-agnostic PROV data model. Specifically, the approach proposes a number of network metrics for provenance data and applies established machine learning techniques over such metrics to build predictive models for some key properties of data. Applying this method to the provenance of real-world data from three different applications, we show that it can successfully identify the owners of provenance documents, assess the quality of crowdsourced data, and identify instructions from chat messages in an alternate-reality game with high levels of accuracy. By so doing, we demonstrate the different ways the proposed provenance network metrics can be used in analysing data, providing the foundation for provenance-based data analytics. Springer 2018-02-15 Article PeerReviewed Huynh, Trung Dong, Ebden, Mark, Fischer, Joel E., Roberts, Stephen and Moreau, Luc (2018) Provenance network analytics: an approach to data analytics using data provenance. Data Mining and Knowledge Discovery . ISSN 1573-756X data provenance; data analytics; network metrics; graph classification https://link.springer.com/article/10.1007/s10618-017-0549-3 doi:10.1007/s10618-017-0549-3 doi:10.1007/s10618-017-0549-3 |
| spellingShingle | data provenance; data analytics; network metrics; graph classification Huynh, Trung Dong Ebden, Mark Fischer, Joel E. Roberts, Stephen Moreau, Luc Provenance network analytics: an approach to data analytics using data provenance |
| title | Provenance network analytics: an approach to data analytics using data provenance |
| title_full | Provenance network analytics: an approach to data analytics using data provenance |
| title_fullStr | Provenance network analytics: an approach to data analytics using data provenance |
| title_full_unstemmed | Provenance network analytics: an approach to data analytics using data provenance |
| title_short | Provenance network analytics: an approach to data analytics using data provenance |
| title_sort | provenance network analytics: an approach to data analytics using data provenance |
| topic | data provenance; data analytics; network metrics; graph classification |
| url | https://eprints.nottingham.ac.uk/48901/ https://eprints.nottingham.ac.uk/48901/ https://eprints.nottingham.ac.uk/48901/ |