FedBench: a benchmark suite for federated semantic data query processing

  • Authors:

  • Michael Schmidt;Olaf Görlitz;Peter Haase;Günter Ladwig;Andreas Schwarte;Thanh Tran

  • Affiliations:

  • Fluid Operations AG, Walldorf, Germany;Institute for Web Science and Technology, University of Koblenz-Landau, Germany;Fluid Operations AG, Walldorf, Germany;Institute AIFB, Karlsruhe Institute of Technology, Germany;Fluid Operations AG, Walldorf, Germany;Institute AIFB, Karlsruhe Institute of Technology, Germany

  • Venue:
  • ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper we present FedBench, a comprehensive benchmark suite for testing and analyzing the performance of federated query processing strategies on semantic data. The major challenge lies in the heterogeneity of semantic data use cases, where applications may face different settings at both the data and query level, such as varying data access interfaces, incomplete knowledge about data sources, availability of different statistics, and varying degrees of query expressiveness. Accounting for this heterogeneity, we present a highly flexible benchmark suite, which can be customized to accommodate a variety of use cases and compare competing approaches. We discuss design decisions, highlight the flexibility in customization, and elaborate on the choice of data and query sets. The practicability of our benchmark is demonstrated by a rigorous evaluation of various application scenarios, where we indicate both the benefits as well as limitations of the state-of-the-art federated query processing strategies for semantic data.