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

THoSP: an Algorithm for Nesting Property Graphs

Breadcrumb

  • Home
  • Research
  • Publications
  • THoSP: an Algorithm for Nesting Property Graphs

Bergami, G. ; Petermann, A. ; Montesi, D.

THoSP: an Algorithm for Nesting Property Graphs

Proc. ACM SIGMOD Workshop on Graph Data Management Experiences & Systems and Network Data Analytics (GRADES-NDA)

2018 / 06

Paper

Abstract

Despite the growing popularity of techniques related to graph summarization, a general operator for the flexible nesting of graphs is still missing. We propose a novel nested graph data model and a powerful graph nesting operator. In contrast to existing approaches, our approach is able to summarize vertices and paths among vertex groups within a single query. Further on, our model supports partial nestings under the preservation of original graph elements as well as the full recovery of the original graph. We propose an efficient nesting algorithm (THoSP) that is able to perform vertex and path nestings in a single visit of the input graph. Results of an experimental evaluation show that THoSP outperforms equivalent implementations based on graph (Cypher, SPARQL), relational (SQL) and document oriented (ArangoDB) databases.

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