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Results for “triple-store”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Alternative Database Designs for the Distribution Common Information Model

The distribution Common Information Model (CIM) offers a means for interoperable distribution system applications. Adoption has been slow in part because of the steep learning curve, and the mismatch between CIM’s object-oriented schema and widely-used relational database schemas. We evaluated alternative database technologies, and tested a triple-store database with SPARQL queries on a set of IEEE distribution test feeders. These alternative queries were much simpler to design and use, so they should help lower one of the important barriers to CIM adoption. An open-source platform has been provided for other developers to work with CIM in this way. One use case is to more rapidly develop interoperable ADMS and DERMS applications. Another use case is to convert feeder models between commercial CIM-compliant products and research software like GridLAB-D and OpenDSS.

lenses, computational modeling, relational databas↗
DOE OSTI2015

A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with ~one trillion edges and ~50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with ~25 billion edges in less than two hours and 2) support database query and visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.

graph visualization↗