Provenance network analytics
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 Wid...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer
2018
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| _version_ | 1797071003705147392 |
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| author | Huynh, T Ebden, M Fischer, J Roberts, S Moreau, L |
| author_facet | Huynh, T Ebden, M Fischer, J Roberts, S Moreau, L |
| author_sort | Huynh, T |
| collection | OXFORD |
| 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 | 2024-03-06T22:47:00Z |
| format | Journal article |
| id | oxford-uuid:5d8608c8-2278-43b6-aa22-715c6fa381c4 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:47:00Z |
| publishDate | 2018 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:5d8608c8-2278-43b6-aa22-715c6fa381c42022-03-26T17:34:57ZProvenance network analyticsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:5d8608c8-2278-43b6-aa22-715c6fa381c4EnglishSymplectic Elements at OxfordSpringer2018Huynh, TEbden, MFischer, JRoberts, SMoreau, LProvenance 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. |
| spellingShingle | Huynh, T Ebden, M Fischer, J Roberts, S Moreau, L Provenance network analytics |
| title | Provenance network analytics |
| title_full | Provenance network analytics |
| title_fullStr | Provenance network analytics |
| title_full_unstemmed | Provenance network analytics |
| title_short | Provenance network analytics |
| title_sort | provenance network analytics |
| work_keys_str_mv | AT huynht provenancenetworkanalytics AT ebdenm provenancenetworkanalytics AT fischerj provenancenetworkanalytics AT robertss provenancenetworkanalytics AT moreaul provenancenetworkanalytics |