Graph Databases are gaining popularity, owing to pervasiveness of graph data in social networks, physical sciences, networking, and web applications. A majority of these databases are based on the property graph model, which is characterized as key/value-based, directed, and multi-relational. In this paper, we consider the problem of supporting property graphs as RDF in Oracle Database. We introduce a property graph to RDF transformation scheme. The main challenge lies in representing the key/value properties of property graph edges in RDF. We propose three models: 1) named graph based, 2) subproperty based, and 3) (extended) reification based, all of which can be supported with RDF capabilities in Oracle Database. These models are evaluated with respect to ease of SPARQL query formulation, join complexities, skewness in generated RDF data, query performance, and storage overhead. An experimental study with a real-life Twitter social network dataset on Oracle Database 12c demonstrates the feasibility of representing property graphs as RDF and presents a quantitative performance comparison of the proposed models.

This paper describes the Semantic Indexing feature introduced in Oracle Database for indexing unstructured text (document) columns. This capability enables searching for concepts (such as people, places, organizations, and events), in addition to words or phrases, with further options for sense disambiguation and term expansion by consulting knowledge captured in OWL/RDF ontologies. The distinguishing aspects of our approach are: 1) Indexing: Instead of building a traditional inverted index of (annotated) token and/or named entity occurrences, we extract the entities, associations, and events present in a text column data and store them as RDF named graphs in the Oracle Database Semantic Store. This base content can be further augmented with knowledge bases and inferred triples (obtained by applying domain-specific ontologies and rule bases). 2) Querying: Instead of relying on proprietary extensions for specifying a search, we allow users to specify a complete SPARQL query pattern that can capture arbitrarily complex relationships between query terms. We have implemented this feature by introducing a sem_contains SQL operator and the associated sem_indextype indexing scheme. The indexing scheme employs an extensible architecture that supports indexing of unstructured text using native as well as third party text extraction tools. The paper presents a model for the semantic index and querying, describes the feature, and outlines its implementation leveraging Oracle's native support for RDF/OWL storage, inferencing, and querying. We also report a study involving use of this feature on a TREC collection of over 130,000 news articles.

Database computers are special-purpose storage and processing devices which are intended to relieve the database management (software) systems running on the general-purpose computers and provide improved storage and processing capabilities (via hardware) for the existing and new database application. However, to support existing database applications, two steps must be followed. First, the existing database must be transformed into the storage format of the new database computer. This one-time tranformation, known as database transformation, is required to preserve the semantics of the database and to take advantage of the advanced hardware features of the new computer. Second, the database sublanguage used in the existing application programs must be supported in real time by the new database computer so that application programs may be executed in the new environment without the need of program conversion. Such real-time translation of sublanguage calls to the instructions of the new database computer, known as query translation, must be straightforward with minimal software support.

Design considerations of a database computer are presented in this paper. The overall architecture of the computer as well as the organization of its individual components are discussed. Several key concepts which are vital to database management are incorporated in the design and organization of the components. The concepts of tracks-in-parallel read-out and logic-per-some-track processing are provided in an on-line database store for the purpose of achieving high-volume content-addressability. The use of auxiliary information about the database for access precision and control has resulted in the design of a structure memory, an array of content-addressable memory and processor pairs, for large collections of indices. The choice of technologies for the implementation of these components is considered in terms of their cost and performance. Modified moving-head disk technology is chosen in order to support the very large on-line database store. Emerging technologies such as magnetic bubbles and CCD's are chosen for the structure memory on the basis of their matching performance with the on-line database store and their capability for parallel-in-blocks-and-serial-within-block processing. Five other important components are also disdissed in the paper. Their role in the database computer and relationship with the structure memory and on-line database store are delineated.

The concepts and capabilities of a database computer (DBC) are given in this paper. The proposed design overcomes many of the traditional problems of database system software and is one of the first to describe a complete data-secure computer capable of handling large databases. This paper begins by characterizing the major problems facing today's database system designers. These problems are intrinsically related to the nature of conventional hardware and can only be solved by introducing new architectural concepts. Several such concepts are brought to bear in the later sections of this paper. These architectural principles have a major impact upon the design of the system and so they are discussed in some detail. A key aspect of these principles is that they can be implemented with near-term technology. The rest of the paper is devoted to the functional characteristics and the theory of operation of the DBC. The theory of operation is based on a series of abstract models of the components and data structures employed by the DBC. These models are used to illustrate how the DBC performs access operations, manages data structures and security specifications, and enforces security requirements. Short Algol-like algorithms are used to show how these operations are carried out. This part of the paper concludes with a high-level description of the DBC organization. The actual details of the DBC hardware are quite involved and so their presentation is not the subject of this paper. A sample database is included in the Appendix to illustrate the working of the security and clustering mechanisms of the DBC.