Techniques for efficiently querying scientific workflow provenance graphs

A key advantage of scientific workflow systems over traditional scripting approaches is their ability to automatically record data and process dependencies introduced during workflow runs. This information is often represented through provenance graphs, which can be used by scientists to better understand, reproduce, and verify scientific results. However, while most systems record and store data and process dependencies, few provide easy-to-use and efficient approaches for accessing and querying provenance information. Instead, users formulate provenance graph queries directly against physical data representations (e.g., relational, XML, or RDF), leading to queries that are difficult to express and expensive to evaluate. We address these problems through a high-level query language tailored for expressing provenance graph queries. The language is based on a general model of provenance supporting scientific workflows that process XML data and employ update semantics. Query constructs ...

• Type: Conference
• Year: 2010
• Where: EDBT
• Authors: Manish Kumar Anand, Shawn Bowers, Bertram Ludäscher
• Keywords: Database, EDBT 2010, Provenance, Provenance Graph, Provenance Graph Queries