Skip to main content

User account menu

  • Log in
DBS-Logo

Database Group Leipzig

within the department of computer science

ScaDS-Logo Logo of the University of Leipzig

Main navigation

  • Home
  • Study
    • Exams
      • Hinweise zu Klausuren
    • Courses
      • Current
    • Modules
    • LOTS-Training
    • Abschlussarbeiten
    • Masterstudiengang Data Science
    • Oberseminare
    • Problemseminare
    • Top-Studierende
  • Research
    • Projects
      • Benchmark datasets for entity resolution
      • FAMER
      • HyGraph
      • Privacy-Preserving Record Linkage
      • GRADOOP
    • Publications
    • Prototypes
    • Annual reports
    • Cooperations
    • Graduations
    • Colloquia
    • Conferences
  • Team
    • Erhard Rahm
    • Member
    • Former employees
    • Associated members
    • Gallery

GRADOOP: Scalable Graph Data Management and Analytics with Hadoop

Breadcrumb

  • Home
  • Research
  • Publications
  • GRADOOP: Scalable Graph Data Management and Analytics with Hadoop

Junghanns, M. ; Petermann, A. ; Gomez, K. ; Rahm, E.

GRADOOP: Scalable Graph Data Management and Analytics with Hadoop

Techn. Report, Univ. of Leipzig, arXiv:1506.00548, June 2015

2015 / 06

Report

Abstract

Many Big Data applications in business and science require the management and analysis of huge amounts of graph data. Previous approaches for graph analytics such as graph databases and parallel graph processing systems (e.g., Pregel) either lack sufficient scalability or flexibility and expressiveness. We are therefore developing a new end-to-end approach for graph data management and analysis based on the Hadoop ecosystem, called Gradoop (Graph analytics on Hadoop). Gradoop is designed around the so-called Extended Property Graph Data Model (EPGM) supporting semantically rich, schema-free graph data within many distinct graphs. A set of high-level operators is provided for analyzing both single graphs and collections of graphs. Based on these operators, we propose a domain-specific language to define analytical workflows. The Gradoop graph store is currently utilizing HBase for distributed storage of graph data in Hadoop clusters. An initial version of Gradoop has been used to analyze graph data for business intelligence and social network analysis.

Recent publications

  • 2025 / 9: Generating Semantically Enriched Mobility Data from Travel Diaries
  • 2025 / 8: Slice it up: Unmasking User Identities in Smartwatch Health Data
  • 2025 / 6: SecUREmatch: Integrating Clerical Review in Privacy-Preserving Record Linkage
  • 2025 / 6: Leveraging foundation models and goal-dependent annotations for automated cell confluence assessment
  • 2025 / 5: Federated Learning With Individualized Privacy Through Client Sampling

Footer menu

  • Directions
  • Contact
  • Impressum