Paper Info
Title
Abstract Provenance Graphs: Anticipating And Exploiting Schema-Level Data Provenance
Abstract
Provenance graphs capture flow and dependency information recorded during scientific workflow runs, which can be used subsequently to interpret, validate, and debug workflow results. In this paper, we propose the new concept of Abstract Provenance Graphs (APGs). APGs are created via static analysis of a configured workflow W and input data schema, i.e., before W is actually executed. They summarize all possible provenance graphs the workflow W can create with input data of type tau, that is, for each input nu epsilon tau there exists a graph homomorphism H(v) between the concrete and abstract provenance graph. APGs are helpful during workflow construction since (1) they make certain workflow design-bugs (e.g., selecting none or wrong input data for the actors) easy to spot; and (2) show the evolution of the overall data organization of a workflow. Moreover, after work-flows have been run, APGs can be used to validate concrete provenance graphs. A more detailed version of this work is available as [14].(1)
Year
DOI
Venue
2010
10.1007/978-3-642-17819-1_23
PROVENANCE AND ANNOTATION OF DATA AND PROCESSES
Keywords
Field
DocType
graph homomorphism,static analysis
Data mining,Existential quantification,Computer science,Graph homomorphism,Static analysis,Database schema,Provenance,Workflow,Schema (psychology),Database,Debugging
Conference
Volume
ISSN
Citations 
6378
0302-9743
5
PageRank 
References 
Authors
0.56
12
2
Name
Order
Citations
PageRank
Daniel Zinn119813.43
Bertram Ludäscher21879239.67