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

Detecting SPARQL Query Templates for Data Prefetching

Johannes Lorey, Felix Naumann
In Proceedings of the 10th Extended Semantic Web Conference (ESWC), Montpellier, France, 2013

Abstract:

Publicly available Linked Data repositories provide a multitude of information. By utilizing SPARQL, Web sites and services can consume this data and present it in a user-friendly form, e.g., in mash-ups. To gather RDF triples for this task, machine agents typically issue similarly structured queries with recurring patterns against the SPARQL endpoint. These queries usually differ only in a small number of individual triple pattern parts, such as resource labels or literals in objects. We present an approach to detect such recurring patterns in queries and introduce the notion of query templates, which represent clusters of similar queries exhibiting these recurrences. We describe a matching algorithm to extract query templates and illustrate the benefits of prefetching data by utilizing these templates. Finally, we comment on the applicability of our approach using results from real-world SPARQL query logs.

BibTeX file

@inproceedings{Johannes2013a,
author = { Johannes Lorey, Felix Naumann },
title = { Detecting SPARQL Query Templates for Data Prefetching },
year = { 2013 },
month = { 0 },
abstract = { Publicly available Linked Data repositories provide a multitude of information. By utilizing SPARQL, Web sites and services can consume this data and present it in a user-friendly form, e.g., in mash-ups. To gather RDF triples for this task, machine agents typically issue similarly structured queries with recurring patterns against the SPARQL endpoint. These queries usually differ only in a small number of individual triple pattern parts, such as resource labels or literals in objects. We present an approach to detect such recurring patterns in queries and introduce the notion of query templates, which represent clusters of similar queries exhibiting these recurrences. We describe a matching algorithm to extract query templates and illustrate the benefits of prefetching data by utilizing these templates. Finally, we comment on the applicability of our approach using results from real-world SPARQL query logs. },
address = { Montpellier, France },
booktitle = { Proceedings of the 10th Extended Semantic Web Conference (ESWC) },
priority = { 0 }
}

Copyright Notice

last change: Fri, 17 Apr 2015 11:27:13 +0200

Teaching Activities

Co-supervised Master's thesis

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