Authors:; Publication Date: 2014-07-11 Research Org.: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States) Sponsoring Org.: USDOE Contributing Org.: Sreenivas R. Sukumar, Nathaniel Bond, and Yifu Zhao (I do not have an email address for Yifu Zhao) OSTI Identifier: 1324317 Report Number(s): Graph Visualization for RDF Graphs with SPARQL; 004920IBMPC00 DOE Contract Number: AC05-00OR22725 Resource Type: Software Software Revision: 00 Software Package Number: 004920 Software CPU: IBMPC Source Code Available: Yes Country of Publication: United States. The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged.
We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluate the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface. The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct 'graph mining' on RDF standard data sets.
We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for 'EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'
Purpose: Visualization and processing of medical images and radiation treatment plan evaluation have traditionally been constrained to local workstations with limited computation power and ability of data sharing and software update. We present a web-based image processing and planning evaluation platform (WIPPEP) for radiotherapy applications with high efficiency, ubiquitous web access, and real-time data sharing. Methods: This software platform consists of three parts: web server, image server and computation server. Each independent server communicates with each other through HTTP requests. The web server is the key component that provides visualizations and user interface through front-end web browsers and relay information to the backend to process user requests.
Fifa 14 crack only v4. The image server serves as a PACS system. The computation server performs the actual image processing and dose calculation. The web server backend is developed using Java Servlets and the frontend is developed using HTML5, Javascript, and jQuery. The image server is based on open source DCME4CHEE PACS system. The computation server can be written in any programming language as long as it can send/receive HTTP requests. Our computation server was implemented in Delphi, Python and PHP, which can process data directly or via a C program DLL.
Results: This software platform is running on a 32-core CPU server virtually hosting the web server, image server, and computation servers separately. Users can visit our internal website with Chrome browser, select a specific patient, visualize image and RT structures belonging to this patient and perform image segmentation running Delphi computation server and Monte Carlo dose calculation on Python or PHP computation server. Conclusion: We have developed a webbased image processing and plan evaluation platform prototype for radiotherapy. This system has clearly demonstrated the feasibility of performing image processing and plan evaluation platform through a web browser and exhibited potential for future cloud based radiotherapy. Brick is a recently proposed metadata schema and ontology for describing building components and the relationships between them.
It represents buildings as directed labeled graphs using the RDF data model. Using the SPARQL query language, building-agnostic applications query a Brick graph to discover the set of resources and relationships they require to operate. Latency-sensitive applications, such as user interfaces, demand response and modelpredictive control, require fast queries — conventionally less than 100ms. We benchmark a set of popular open-source and commercial SPARQL databases against three real Brick models using seven application queries and find that none of them meet this performance target. This lack of performance can be attributed to design decisions that optimize for queries over large graphs consisting of billions of triples, but give poor spatial locality and join performance on the small dense graphs typical of Brick.
We present the design and evaluation of HodDB, a RDF/SPARQL database for Brick built over a node-based index structure. HodDB performs Brick queries 3-700x faster than leading SPARQL databases and consistently meets the 100ms threshold, enabling the portability of important latency-sensitive building applications. Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER.Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools.
Rdf Graph Visualization Tools For Beginners
This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is 4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns.
Knowledge Graph Visualization Tool
Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.
RDF Data Visualization RDF Data VisualizationThere was a on.I was checking out the options and figured I'd record them here so others could be as lazy as I would have liked to have been.I downloaded.git clone into 'ontology-visualization'.remote: Counting objects: 44, done.remote: Compressing objects: 100% (31/31), done.remote: Total 44 (delta 19), reused 34 (delta 12), pack-reused 0Unpacking objects: 100% (44/44), done.It came with a:: John a: Man;: name 'John';: hasSpouse: Mary.: Mary a: Woman;: name 'Mary';: hasSpouse: John.: Johnjr a: Man;: name 'John Jr.' ;: hasParent: John,: Mary.: TimeSpan a owl: Class.: event a: Activity;: hastimespan a: TimeSpan;: atsometimewithindate '2018-01-12' ^^xsd:date.: u129u-klejkajo-2309124u-sajfl a: Person;: name 'John Doe'.I installed the ( sudo pip install rdflib) and rendered it with ontology-visualization:python./ontologyviz.py -o test.dot test.ttl -O ontology.ttlWARNING Class doesn't exist in the ontology!WARNING Property doesn't exist in the ontology!dot -Tsvg -o test-ontology-visualization.svg test.dot.