Name
Abstract | ||
|---|---|---|
Provenance Network Analytics is a method of analyzing provenance that assesses a collection of provenance graphs by training a machine learning algorithm to make predictions about the characteristics of data artifacts based on their provenance graph metrics. The shape of a provenance graph can vary according the modelling approach chosen by data analysts, and this is likely to affect the accuracy of machine learning algorithms, so we propose a framework for capturing provenance using semantic web technologies to allow use of multiple provenance models at runtime in order to test their effects. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-98379-0_29 | PROVENANCE AND ANNOTATION OF DATA AND PROCESSES, IPAW 2018 |
Keywords | Field | DocType |
Graph, Network, Analytics | Data mining,Graph,Network analytics,Information retrieval,Computer science,Semantic Web,Provenance,Analytics | Conference |
Volume | ISSN | Citations |
11017 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bernard Roper | 1 | 0 | 0.34 |
| Adriane Chapman | 2 | 382 | 27.65 |
| David J. Martin | 3 | 4 | 3.21 |
| Jeremy Morley | 4 | 27 | 3.85 |