The Bigdata® RDF Graph Database

2014 ◽  
pp. 193-237 ◽  
Author(s):  
Bryan Thompson ◽  
Mike Personick ◽  
Martyn Cutcher
Keyword(s):  
Graph Database ◽  
Rdf Graph

Introducing Acacia-RDF: An X10-Based Scalable Distributed RDF Graph Database Engine

2016 ◽  
Author(s):  
Miyuru Dayarathna ◽  
Isuru Herath ◽  
Yasima Dewmini ◽  
Gayan Mettananda ◽  
Sameera Nandasiri ◽  
...  
Keyword(s):  
Graph Database ◽  
Rdf Graph ◽  
Database Engine

Acacia-RDF: An X10-Based Scalable Distributed RDF Graph Database Engine

2016 ◽  
Author(s):  
Miyuru Dayarathna ◽  
Isuru Herath ◽  
Yasima Dewmini ◽  
Gayan Mettananda ◽  
Sameera Nandasiri ◽  
...  
Keyword(s):  
Graph Database ◽  
Rdf Graph ◽  
Database Engine

RDF* Graph Database as Interlingua for the TextWorld Challenge

2019 ◽  
Author(s):  
Guntis Barzdins ◽  
Didzis Gosko ◽  
Paulis F. Barzdins ◽  
Uldis Lavrinovics ◽  
Gints Bernans ◽  
...  
Keyword(s):  
Graph Database ◽  
Rdf Graph

scholarly journals DESIGN AND EVALUATION OF A BIM-GIS INTEGRATED INFORMATION MODEL USING RDF GRAPH DATABASE

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 175-182
Author(s):  
A.-H. Hor ◽  
G. Sohn
Keyword(s):  
Performance Metrics ◽  
Graph Model ◽  
Information Model ◽  
Query Languages ◽  
Semantic Integration ◽  
Graph Database ◽  
Graph Databases ◽  
Rdf Graph ◽  
Functional Components ◽  
Integrated Information

Abstract. The semantic integration modeling of BIM industry foundations classes and GIS City-geographic markup language are a milestone for many applications that involve both domains of knowledge. In this paper, we propose a system design architecture, and implementation of Extraction, Transformation and Loading (ETL) workflows of BIM and GIS model into RDF graph database model, these workflows were created from functional components and ontological frameworks supporting RDF SPARQL and graph databases Cypher query languages. This paper is about full understanding of whether RDF graph database is suitable for a BIM-GIS integrated information model, and it looks deeper into the assessment of translation workflows and evaluating performance metrics of a BIM-GIS integrated data model managed in an RDF graph database, the process requires designing and developing various pipelines of workflows with semantic tools in order to get the data and its structure into an appropriate format and demonstrate the potential of using RDF graph databases to integrate, manage and analyze information and relationships from both GIS and BIM models, the study also has introduced the concepts of Graph-Model occupancy indexes of nodes, attributes and relationships to measure queries outputs and giving insights on data richness and performance of the resulting BIM-GIS semantically integrated model.

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Daniele D’Agostino ◽  
Pietro Liò ◽  
Marco Aldinucci ◽  
Ivan Merelli
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Cell Types ◽  
Graph Database ◽  
Graph Databases ◽  
Sources Of Information ◽  
Chromosome Conformation ◽  
Wide Scale ◽  
User Friendly ◽  
Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

Sign in / Sign up

Export Citation Format

Share Document