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Proceedings ArticleDOI

An Empirical Comparison of Graph Databases

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TLDR
A distributed graph database comparison framework is presented and the results obtained by comparing four important players in the graph databases market: Neo4j, Orient DB, Titan and DEX are presented.
Abstract
In recent years, more and more companies provide services that can not be anymore achieved efficiently using relational databases. As such, these companies are forced to use alternative database models such as XML databases, object-oriented databases, document-oriented databases and, more recently graph databases. Graph databases only exist for a few years. Although there have been some comparison attempts, they are mostly focused on certain aspects only. In this paper, we present a distributed graph database comparison framework and the results we obtained by comparing four important players in the graph databases market: Neo4j, Orient DB, Titan and DEX.

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Citations
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Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries.

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Risk mitigation strategies for critical infrastructures based on graph centrality analysis

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miTALOS v2: Analyzing Tissue Specific microRNA Function.

TL;DR: A novel methodology for tissue specific pathway analysis of miRNAs was developed, which incorporated the most recent and highest quality miRNA targeting data, RNA-seq based gene expression data and multiple new pathway data sources to increase the biological relevance of the predicted miRNA-pathway associations.
References
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TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Book

Graphs and hypergraphs

Claude Berge
Proceedings ArticleDOI

A Comparison of Current Graph Database Models

Renzo Angles
TL;DR: A systematic comparison of current graph database models is presented and includes general features (for data storing and querying), data modeling features (i.e., data structures, query languages, and integrity constraints), and the support for essential graph queries.
Journal ArticleDOI

The meaningful use of big data: four perspectives -- four challenges

TL;DR: Twenty-five Semantic Web and Database researchers met at the 2011 STI Semantic Summit in Riga, Latvia July 6-8, 2011 to discuss the opportunities and challenges posed by Big Data.
Journal ArticleDOI

Unraveling protein networks with power graph analysis.

TL;DR: Power graph analysis as discussed by the authors is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs.