RDFBroker: A Signature-Based High-Performance RDF Store

Abstract

Abstract. Many approaches for RDF stores exist, most of them using very straight-forward techniques to store triples in or mapping RDF Schema classes to database tables. In this paper we propose an RDF store that uses a natural mapping of RDF resources to database tables that does not rely on RDF Schema, but constructs a schema based on the occurring signatures, where a signature is the set of properties used on a resource. This technique can therefore be used for arbitrary RDF data, i.e., RDF Schema or any other schema/ontology language on top of RDF is not required. Our approach can be used for both in-memory and on-disk relational database-based RDF store implementations.A first prototype has been implemented and already shows a significant performance increase compared to other freely available (inmemory) RDF stores.

An Intermediate Algebra for Optimizing RDF Graph Pattern Matching on MapReduce

“…[20], investigates several join algorithms which leverage pre-processing techniques on Hadoop, but mainly focus on log processing. RDFBroker [21] is a RDF store that is based on the concept of a signature (set of properties of a resource), similar to NTGA's structurelabeling function λ. However, the focus of [21] is to provide a natural way to map RDF data to database tables, without presuming schema knowledge.…”

“…RDFBroker [21] is a RDF store that is based on the concept of a signature (set of properties of a resource), similar to NTGA's structurelabeling function λ. However, the focus of [21] is to provide a natural way to map RDF data to database tables, without presuming schema knowledge. Pregel [22] and Signal/Collect [23] provide graph-oriented primitives as opposed to relational algebra type operators, and also target parallel platforms.…”

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

“…The vertically partitioned approach can also be data-driven (e.g., implemented in Sesame [23]): property relations are created when their instances are first seen in an RDF document during data mapping. RDF store RDFBroker [100] implements signature relations, which are conceptually similar to clustered property relations, but are generated based on RDF data rather than RDF Schema information. There is a lack of experiments that compare the data-driven approach with other approaches (except for the vertically partitioned approach that is available in both schema-aware and data-driven flavors).…”

“…There is a lack of experiments that compare the data-driven approach with other approaches (except for the vertically partitioned approach that is available in both schema-aware and data-driven flavors). RDFBroker [100] reports improved in-memory query performance over Sesame and Jena for some test queries. Schema evolution for the data-driven approach, if supported, might be expensive.…”

“…Our motivation of using the mature relational database technology is provided by the fact that provenance metadata growth rate is potentially very high since provenance is generated automatically for every scientific experiment. On the Semantic Web, large volumes of RDF data are managed with the so called RDF stores, and majority of them, including Jena [118,119], Sesame [23], 3store [56,57], KAON [107], RStar [71], OpenLink Virtuoso [42], DLDB [81], RDFSuite [9,105], DBOWL [77], PARKA [101], and RDFBroker [100], use an RDBMS as a backend to manage RDF data. Although a general-purpose relational RDF store (see [15] for a survey) can be used for provenance metadata management, the following provenance-specific requirements bring about several optimization strategies for schema design, data mapping, and query mapping, enabling us to develop a provenance metadata management system that is more efficient and flexible than one that is simply based on an existing RDF store.…”