A Semantics-Oriented Storage Model for Big Heterogeneous RDF Data

Abstract. Increasing availability of RDF data covering different domains is en-abling ad-hoc integration of different kinds of data to suit varying needs. This usually results in large collections of data such as the Billion Triple Challenge datasets or SNOMED CT, that are not just “big ” in the sense of volume but also “big ” in variety of property and class types. However, techniques used by most RDF data processing systems fail to scale adequately in these scenarios. One major reason is that the storage models adopted by most of these systems, e.g., vertical partitioning, do not align well with the semantic units in the data and queries. While Big Data distributed processing platforms such as the Hadoop-based platforms offer the promise of “unlimited scale-out processing”, there are still open questions as to how best to physically partition and distribute RDF data for optimized distributed processing. In this poster, we present the idea of a semantics-oriented RDF storage model that partitions data into logical units that map to subqueries in graph patterns. These logical units can be seen as equiva-lence classes of star subgraphs in an RDF graph. This logical partitioning strategy enables more aggressive pruning of irrelevant query results by pruning irrelevant partitions. It also enables the possibility of semantic-query optimization for some queries such as eliminating joins under appropriate conditions. These benefits in addition to appropriate techniques for physically partitioning the logical parti-tions, translate to improved performance as shown by some preliminary results