Distributed Semantic Web Data Management in HBase and MySQL Cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.Comment: In Proc. of the 4th IEEE International Conference on Cloud Computing (CLOUD'11

Distributed Semantic Web data management in HBase and MySQL cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C\u27s Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management

Google Earth Visualizations: Preview and Delivery of Hydrographic and Other Marine Datasets

Existing hydrographic data analysis and visualization tools are very powerful, but lack easy access to web data management tools. Virtual globe software provides a gateway to a host of important data products in formats usable by specialized tools such as CARIS, Fledermaus, and Arc/Info. With virtual globe interfaces, users see complimentary and consistent geographic representations of available data in an easy-tonavigate format. We present a preview of visualizations that build upon virtual globe software. These examples are viewed in Google Earth, but could also be implemented in a number of alternative programs (e.g. NASA World Wind, Dapple, OSSIM Planet). We have assembled Google Earth visualizations from three datasets to illustrate each of the four primary types of data (handle point, line, area, and time data). The USCG Marine Information for Safety and Law Enforcement (MISLE) database of ship incidents illustrates point data. A short sample of the USCG National Automatic Identification System logs (N-AIS) demonstrates rendering of line data. Area data is exemplified in the United Nations Convention f the Law of the Sea (UNCLOS) multibeam bathymetry. Point, line and area data are combined to present a preview of S57 chart information. Finally, the MISLE database uses time to show maritime incidents that occurred in US waterways. The visualizations for our initial work were created with hand coding and small scripts. However, tools such as Fledermaus and RockWare have added Google Earth export functionality that makes authoring Google Earth resources easy to construct. For large dataset that require additional processing and analyses, Google Earth visualizations can offer users a range of download formats and suggest what software to use. We believe that this virtual globe-based-approach can make geospatial data sets more widely accessible via the world-wide-web

RDFViewS: A Storage Tuning Wizard for RDF Applications

In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views to materialize, in order to minimize the query response time for a specific SPARQL query workload, while taking into account the view maintenance cost and storage space constraints. Our system employs practical algorithms and heuristics to navigate through the search space of potential view configurations, and exploits the possibly available semantic information - expressed via an RDF Schema - to ensure the completeness of the query evaluation