Presented in this paper is the data model for ORION, a prototype database system that adds persistence and sharability to objects created and manipulated in object-oriented applications. The ORION data model consolidates and modifies a number of major concepts found in many objectoriented systems, such as objects, classes, class lattice, methods, and inheritance. These concepts are reviewed and three major enhancements to the conventional object-oriented data model, namely, schema evolution, composite objects, and versions, are elaborated upon. Schema evolution is the ability to dynamically make changes to the class definitions and the structure of the class lattice. Composite objects are recursive collections of exclusive components that are treated as units of storage, retrieval, and integrity enforcement. Versions are variations of the same object that are related by the history of their derivation. These enhancements are strongly motivated by the data management requirements of the ORION applications from the domains of artificial intelligence, computer-aided design and manufacturing, and office information systems with multimedia documents.
We present RDFox-a main-memory, scalable, centralised RDF store that supports materialisation-based parallel datalog reasoning and SPARQL query answering. RDFox uses novel and highly-efficient parallel reasoning algorithms for the computation and incremental update of datalog materialisations with efficient handling of owl:sameAs. In this system description paper, we present an overview of the system architecture and highlight the main ideas behind our indexing data structures and our novel reasoning algorithms. In addition, we evaluate RDFox on a high-end SPARC T5-8 server with 128 physical cores and 4TB of RAM. Our results show that RDFox can effectively exploit such a machine, achieving speedups of up to 87 times, storage of up to 9.2 billion triples, memory usage as low as 36.9 bytes per triple, importation rates of up to 1 million triples per second, and reasoning rates of up to 6.1 million triples per second.
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