Provgen: Generating synthetic PROV graphs with predictable structure

Hugo Firth*, Paolo Missier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper introduces provGen, a generator aimed at producing large synthetic provenance graphs with predictable properties and of arbitrary size. Synthetic provenance graphs serve two main purposes. Firstly, they provide a variety of controlled workloads that can be used to test storage and query capabilities of provenance management systems at scale. Secondly, they provide challenging testbeds for experimenting with graph algorithms for provenance analytics, an area of increasing research interest. provGen produces PROV graphs and stores them in a graph DBMS (Neo4J). A key feature is to let users control the relationship makeup and topological features of the graph, by providing a seed provenance pattern along with a set of constraints, expressed using a custom Domain Specific Language. We also propose a simple method for evaluating the quality of the generated graphs, by measuring how realistically they simulate the structure of real-world patterns.

Original languageEnglish
Title of host publicationProvenance and Annotation of Data and Processes - 5th International Provenance and Annotation Workshop, IPAW 2014, Revised Selected Papers
EditorsBeth Plale, Bertram Ludäscher, Bertram Ludäscher
PublisherSpringer Verlag
Pages16-27
Number of pages12
ISBN (Electronic)9783319164618
DOIs
Publication statusPublished - 2015
Event5th International Provenance and Annotation Workshop, IPAW 2014 - Cologne, Germany
Duration: 10 Jun 201411 Jun 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8628
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Provenance and Annotation Workshop, IPAW 2014
Country/TerritoryGermany
CityCologne
Period10/06/1411/06/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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