11/4/2007 Comparing path-based and vertically-partitioned RDF databases

Given the increasing prevalence of RDF data formats for storing and sharing data on the Semantic Web, efficient storage mechanisms for RDF data are also becoming increasingly important. We survey existing storage solutions for RDF data in an RDMS. Two recent and novel storage concepts open the door for significantly better querying efficiency. The first, proposed by Matono, et al (2005), models RDF data as a graph, then stores materialized path expressions for efficient querying. The second, proposed by Abadi, et al (2007), stores RDF triples in a vertically-partitioned column-oriented database, in which each RDF property is stored in a single table. Our objective is to compare these two storage methods to find relative strengths and weaknesses on a wide range of possible queries. Related Work The simplest way to store RDF data is in a triple store, essentially one large table with three columns for subject, predicate, and object. Variations on the triple-store have shown improvements in efficiency and reduced the number of self-joins needed when issuing complex queries. Data Normalization A normalized triple store attempts to improve the efficiency of the triple store. A Statements table