Name
Abstract | ||
|---|---|---|
It is rare for data's history to include computational processes alone. Even when software generates data, users ultimately decide to execute software procedures, choose their configuration and inputs, reconfigure, halt and restart processes, and so on. Understanding the provenance of data thus involves understanding the reasoning of users behind these decisions, but demanding that users explicitly document decisions could be intrusive if implemented naively, and impractical in some cases. In this paper, therefore, we explore an approach to transparently deriving the provenance of user decisions at query time. The user reasoning is simulated, and if the result of the simulation matches the documented decision, the simulation is taken to approximate the actual reasoning. The plausibility of this approach requires that the simulation mirror human decision-making, so we adopt an automated process explicitly modelled on human psychology. The provenance of the decision is modelled in Open Provenance Model (OPM), allowing it to be queried as part of a larger provenance graph, and an OPM profile is provided to allow consistent querying of provenance across user decisions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34222-6_9 | IPAW |
Keywords | Field | DocType |
open provenance model,software procedure,larger provenance graph,human psychology,human decision-making,user reasoning,opm profile,transparent provenance derivation,automated process,actual reasoning,user decision | Graph,Data mining,Inference,Computer science,Provenance,Software,Database | Conference |
Volume | ISSN | Citations |
7525 | 0302-9743 | 3 |
PageRank | References | Authors |
0.38 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ingrid Nunes | 1 | 198 | 29.38 |
| Yuhui Chen | 2 | 13 | 4.26 |
| Simon Miles | 3 | 1599 | 109.29 |
| Michael Luck | 4 | 3440 | 275.97 |
| Carlos Lucena | 5 | 589 | 41.51 |