Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Dr. Johannes Lorey

 

 

Former PhD student of the HPI Research School
at University of Potsdam
Email: Johannes Lorey

 


 

 

Research Activities

  • Cloud Computing
  • Large-Scale Data Analysis and Processing
  • Data Placement
  • Linked Data Management

Publications

SPARQL Endpoint Metrics for Quality-Aware Linked Data Consumption

Johannes Lorey
In Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS '13), Vienna, Austria, 2013

Abstract:

In recent years, dozens of publicly accessible Linked Data repositories containing vast amounts of knowledge presented in the Resource Description Framework (RDF) format have been set up worldwide. By utilizing the SPARQL query language, users can consume, integrate, and present data from a federation of sources for different application scenarios. However, several challenges arise for distributed query processing across multiple SPARQL endpoints, such as devising suitable query optimization or result caching strategies. For implementing these techniques, one crucial aspect is determining appropriate endpoint features. In this work, we introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. We present comprehensive approaches for deriving these metrics and validate them through extensive evaluation on real-world SPARQL endpoints. Finally, we discuss possible implications of our findings for data consumers.

BibTeX file

@inproceedings{Johannes2013a,
author = { Johannes Lorey },
title = { SPARQL Endpoint Metrics for Quality-Aware Linked Data Consumption },
year = { 2013 },
month = { 0 },
abstract = { In recent years, dozens of publicly accessible Linked Data repositories containing vast amounts of knowledge presented in the Resource Description Framework (RDF) format have been set up worldwide. By utilizing the SPARQL query language, users can consume, integrate, and present data from a federation of sources for different application scenarios. However, several challenges arise for distributed query processing across multiple SPARQL endpoints, such as devising suitable query optimization or result caching strategies. For implementing these techniques, one crucial aspect is determining appropriate endpoint features. In this work, we introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. We present comprehensive approaches for deriving these metrics and validate them through extensive evaluation on real-world SPARQL endpoints. Finally, we discuss possible implications of our findings for data consumers. },
address = { Vienna, Austria },
booktitle = { Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS '13) },
isbn = { 978-1-4503-2113-6 },
priority = { 0 }
}

Copyright Notice

last change: Tue, 10 Dec 2013 00:11:12 +0100

Teaching Activities

Co-supervised Master's thesis

  • Armin Zamani-FarahaniStrategies for structure-based rewriting of SPARQL queries for data prefetching, 2013