Indexing Execution Patterns in Workflow Provenance Graphs through Generalized Trie Structures

TL;DR

This paper introduces a novel generalized trie structure to index and analyze scientific workflow execution provenance, improving search and analysis capabilities over existing methods.

Contribution

It presents a statistically enriched trie-based approach to exploit workflow provenance data, bridging the gap between workflow specifications and actual execution traces.

Findings

Outperforms SPARQL 1.1 Property Paths in querying provenance graphs

Enhances workflow search and analysis through new indexing techniques

Bridges the gap between workflow descriptions and execution data

Abstract

Over the last years, scientific workflows have become mature enough to be used in a production style. However, despite the increasing maturity, there is still a shortage of tools for searching, adapting, and reusing workflows that hinders a more generalized adoption by the scientific communities. Indeed, due to the limited availability of machine-readable scientific metadata and the heterogeneity of workflow specification formats and representations, new ways to leverage alternative sources of information that complement existing approaches are needed. In this paper we address such limitations by applying statistically enriched generalized trie structures to exploit workflow execution provenance information in order to assist the analysis, indexing and search of scientific workflows. Our method bridges the gap between the description of what a workflow is supposed to do according to its specification and related metadata and what it actually does as recorded in its provenance execution trace. In doing so, we also prove that the proposed method outperforms SPARQL 1.1 Property Paths for querying provenance graphs.