On the Statistical Analysis of Practical SPARQL Queries

TL;DR

This paper provides a statistical analysis of practical SPARQL queries, examining their structural and semantic features to inform better query processing and benchmark development.

Contribution

It introduces a comprehensive statistical and semantic characterization of real-world SPARQL queries, aiding the development of heuristics and optimized engines.

Findings

Analysis of occurrence frequency of triple patterns and fragments

Identification of semantic properties like monotonicity and satisfiability

Insights to improve SPARQL query processing and benchmarking

Abstract

In this paper, we analyze some basic features of SPARQL queries coming from our practical world in a statistical way. These features include three statistic features such as the occurrence frequency of triple patterns, fragments, well-designed patterns and four semantic features such as monotonicity, non-monotonicity, weak monotonicity (old solutions are still served as parts of new solutions when some new triples are added) and satisfiability. All these features contribute to characterize SPARQL queries in different dimensions. We hope that this statistical analysis would provide some useful observation for researchers and engineers who are interested in what practical SPARQL queries look like, so that they could develop some practical heuristics for processing SPARQL queries and build SPARQL query processing engines and benchmarks. Besides, they can narrow the scope of their problems by avoiding those cases that do possibly not happen in our practical world.