The paper addresses the problem of visualizing large scale RDF data via a 3-S approach, namely, by using, (1) Subsets: to present only relevant data for visualisation; both static and dynamic subsets can be specified, (2) Summaries: to capture the essence of RDF data being viewed; summarized data can be expanded on demand thereby allowing users to create hybrid (summary-detail) fisheye views of RDF data, and (3) Sampling: to further optimize visualization of large-scale data where a representative sample suffices. The visualization scheme works with both asserted and inferred triples (generated using RDF(S) and OWL semantics). This scheme is implemented in Oracle by developing a plug-in for the Cytoscape graph visualization tool, which uses functions defined in a Oracle PL/SQL package, to provide fast and optimized access to Oracle Semantic Store containing RDF data. Interactive visualization of a synthesized RDF data set (LUBM 1 million triples), two native RDF datasets (Wikipedia 47 million triples and UniProt 700 million triples), and an OWL ontology (eClassOwl with a large class hierarchy including over 25,000 OWL classes, 5,000 properties, and 400,000 class-properties) demonstrates the effectiveness of our visualization scheme.

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.

XACML has emerged as a popular access control language on the Web, but because of its rich expressiveness, it has proved difficult to analyze in an automated fashion. In this paper, we present a formalization of XACML using description logics (DL), which are a decidable fragment of First-Order logic. This formalization allows us to cover a more expressive subset of XACML than propositional logic-based analysis tools, and in addition we provide a new analysis service (policy redundancy). Also, mapping XACML to description logics allows us to use off-the-shelf DL reasoners for analysis tasks such as policy comparison, verification and querying. We provide empirical evaluation of a policy analysis tool that was implemented on top of open source DL reasoner Pellet.