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

Identifying and Determining SPARQL Endpoint Characteristics

Johannes Lorey
International Journal of Web Information Systems, vol. 10(3) 2014

Abstract:

Publicly accessible SPARQL endpoints contain vast amounts of knowledge from a large variety of domains. Utilizing the structured query language, users can consume, integrate, and present data from such Linked Data sources for different application scenarios. However, oftentimes these endpoints are not configured to process specific workloads as efficiently as possible. Implemented restrictions further impede data consumption, e.g., by limiting the number of results returned per request. Assisting users in leveraging SPARQL endpoints requires insight into functional and non-functional properties of these knowledge bases. 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

@article{Johannes2014a,
author = { Johannes Lorey },
title = { Identifying and Determining SPARQL Endpoint Characteristics },
journal = { International Journal of Web Information Systems },
year = { 2014 },
volume = { 10 },
number = { 3 },
month = { 0 },
abstract = { Publicly accessible SPARQL endpoints contain vast amounts of knowledge from a large variety of domains. Utilizing the structured query language, users can consume, integrate, and present data from such Linked Data sources for different application scenarios. However, oftentimes these endpoints are not configured to process specific workloads as efficiently as possible. Implemented restrictions further impede data consumption, e.g., by limiting the number of results returned per request. Assisting users in leveraging SPARQL endpoints requires insight into functional and non-functional properties of these knowledge bases. 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 },
publisher = { Emerald Group Publishing },
issn = { 1744-0084 },
priority = { 0 }
}

Copyright Notice

last change: Tue, 14 Apr 2015 17:51:57 +0200

Teaching Activities

Co-supervised Master's thesis

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