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

AMC – A Framework for Modelling and Comparing Matching Systems as Matching Processes

Breadcrumb

  • Home
  • AMC – A Framework for Modelling and Comparing Matching Systems as Matching Processes

Peukert, E. ; Eberius, J. ; Rahm, E.

AMC – A Framework for Modelling and Comparing Matching Systems as Matching Processes

Proc. Int. Conf. on Data Engineering (Demo paper), 2011

2011 / 04

Andere

Abstract

We present the Auto Mapping Core (AMC), a new
framework that supports fast construction and tuning of schema
matching approaches for specific domains such as ontology
alignment, model matching or database-schema matching.
Distinctive features of our framework are new visualisation
techniques for modelling matching processes, stepwise tuning of
parameters, intermediate result analysis and performanceoriented
rewrites. Furthermore, existing matchers can be
plugged into the framework to comparatively evaluate them in a
common environment. This allows deeper analysis of behaviour
and shortcomings in existing complex matching systems.

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 / 7: MPGT: Multimodal Physics-Constrained Graph Transformer Learning for Hybrid Digital Twins
  • 2025 / 6: Leveraging foundation models and goal-dependent annotations for automated cell confluence assessment
  • 2025 / 6: SecUREmatch: Integrating Clerical Review in Privacy-Preserving Record Linkage

Footer menu

  • Directions
  • Contact
  • Impressum