The growth of RDF data makes it imperative that an efficient mechanism for bulk-loading RDF graphs be supported. Thus, the paper proposes a bulk-load scheme that allows fast loading of arbitrarily large RDF graphs into a database. Specifically, three modes of load are supported: i) loading into an empty RDF graph, ii) appending to a non-empty RDF graph, and iii) concurrent loads into multiple graphs. The bulk-load scheme is implemented as part of Oracle database semantic technologies and the performance experiments conducted with a variety of RDF graphs (from UniProt and synthesized data of Lehigh University Benchmark) demonstrate the scalability of the approach. The paper outlines the challenges involved in bulk- loading of large RDF graphs, describes the bulk-load scheme, discusses its implementation, and presents a performance study.

This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.

Content-based retrieval of images is the ability to retrieve images that are similar to a query image. Oracle8i Visual Information Retrieval provides this facility based on technology licensed from Virage, Inc. This product is built on top of Oracle8i interMedia which enables storage, retrieval and management of images, audios and videos. Images are matched using attributes such as color, texture and structure and efficient content-based retrieval is provided using indexes of an image index type. The design of the index type is based on a multi-level filtering algorithm. The filters reduce the search space so that the expensive comparison algorithm operates on a small subset of the data. Bitmap indexes are used to evaluate the first filter resulting in a design which performs well and is scalable. The image index type is built using Oracle8i extensible indexing technology, allowing users to create, use, and drop instances of this index type as they would any other standard index. In this paper we present an overview of the product, the design of the image index type, and some performance results of our product.