Many RDF Knowledge Graph Stores use IDs to represent triples to save storage and for ease of maintenance. Oracle is no exception. While this design is good for a small footprint on disk, it incurs overhead in query processing, as it requires joins with the value table to return results or process aggregates/filter/order-by queries. It becomes especially problematic as the result size increases or the number of projected variables increases. Depending on queries, the value table join could take up most of the query processing time. In this paper, we propose to use in-memory virtual columns to avoid value table joins. It has advantages in that it does not increase the footprint on disk, and at the same time it avoids the value table joins by utilizing in-memory virtual columns. The idea is to materialize the values only in memory and utilize the compression and vector processing that come with Oracle Database In-Memory technology. Typically, the value table in RDF is small compared to the triples table. Therefore, its footprint is manageable in memory, especially with compression in columnar format. The same mechanism can be applied to any application where there exists a one-to-one mapping between an ID and its value, such as data warehousing or data marts. The mechanism has been implemented in Oracle 18c. Experimental results using the LUBM1000 benchmark show up to two orders of magnitude query performance improvement.

Graphics Processing Units (GPUs) are gaining notable popularity due to their affordable high performance multi-core architecture. They are particularly useful for massive computations that involve large data sets. In this paper, we present a highly scalable approach for the NP-hard Vertex Cover problem. Our method is based on an advanced data structure to reduce memory usage for more parallelism and we propose a load balancing scheme that is effective for multiGPU architectures. Our parallel algorithm was implemented on multiple AMD GPUs using OpenCL. Experimental results show that our proposed approach can achieve significant speedups on the hard instances of the DIMACS benchmarks as well as the notoriously hard 120-Cell graph and its variants.

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.

The W3C's Semantic Web Activity is illustrating the use of semantics for information integration, search, and analysis. However, the majority of the work in this community has focused more on the thematic aspects of information and has paid less attention to its spatial and temporal dimensions. In this paper, we present an integrative ontology-based framework incorporating the thematic, spatial, and temporal dimensions of information. This framework is built around the RDF metadata model. Our ultimate goal is to provide an information system which allows searching and analysis of relationships in any or all of the three dimensions of space, time, and theme. Toward this end, we present an upper-level ontology combining concepts and relationships from both the thematic and spatial dimensions and show how to incorporate temporal semantics into this ontology. We also introduce the notion of a thematic context linking entities of differing dimensions and define a set of query operators built upon these contexts.