This paper reports on the results of an independent evaluation of the
techniques presented in the VLDB 2007 paper "Scalable Semantic Web Data
Management Using Vertical Partitioning", authored by D. Abadi, A. Marcus, S.
R. Madden, and K. Hollenbach. We revisit the proposed benchmark and examine
both the data and query space coverage. The benchmark is extended to cover a
larger portion of the query space in a canonical way. Repeatability of the
experiments is assessed using the code base obtained from the authors.
Inspired by the proposed vertically-partitioned storage solution for RDF
data and the performance figures using a column-store, we conduct a
complementary analy- sis of state-of-the-art RDF storage solutions. To this
end, we employ MonetDB/SQL, a fully-functional open source column-store, and
a well-known --- for its performance --- commercial row-store DBMS.We
implement two relational RDF storage solutions – triple-store and
vertically-partitioned --- in both systems. This allows us to expand the
scope of with the performance characterization along both dimensions ---
triple-store vs. vertically-partitioned and row-store vs. column-store ---
individually, before analyzing their combined effects. A detailed report of
the experimental test-bed, as well as an in-depth analysis of the parameters
involved, clarify the scope of the solution originally presented and
position the results in a broader context by covering more systems