DAISY - Center for Data-intensive Systems

Our colleague Kashif Rabbani will present the paper "Optimizing SPARQL Queries using Shape Statistics" in collaboration with Matteo Lissandrini and Katja Hose at The 24th International Conference on Extending Database Technology (EDBT2021) https://edbticdt2021.cs.ucy.ac.cy in the Graph Management session on Friday at 17:45.

Find more details about the paper, including the presentation and the link to the source code here: https://relweb.cs.aau.dk/rdfshapes/
Find the pdf here:https://relweb.cs.aau.dk/rdfshapes/files/edbt2021.pdf

ABSTRACT
With the growing popularity of storing data in native RDF, we witness more and more diverse use cases with complex SPARQL queries. As a consequence, query optimization and in particular cardinality estimation and join ordering becomes even more crucial. Classical methods exploit global statistics covering the entire RDF graph as a whole, which naturally fails to correctly capture correlations that are very common in RDF datasets, which then leads to erroneous cardinality estimations and suboptimal query ex*****on plans. The alternative of trying to capture correlations in a fine-granular manner, on the other hand, results in very costly preprocessing steps to create these statistics. Hence, in this paper, we propose shapes statistics, which extend the recent SHACL standard with statistic information to capture the correlation between classes and properties. Our extensive experiments on synthetic and real data show that shapes statistics can be generated and managed with only little overhead without disadvantages in query runtime while leading to noticeable improvements in cardinality estimation.

Optimizing SPARQL Queries using Shape Statistics - EDBT 2021 - Teaser A short video about work.