Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB

Fernandes, Diogo Lüders; Bernardino, Jorge

doi:10.5220/0006910203730380

Cited by 86 publications

(58 citation statements)

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“…In its Community free edition (Apache 2 License), it does not support features, such as fault tolerance, horizontal scalability, clustering, sharding and replication. However, in its Enterprise paid edition, it supports all the features previously mentioned [29].…”

Section: Orientdbmentioning

confidence: 98%

“…Neo4j is a graph NoSQL database system. Its data model prioritizes relationships between entities in the form of graphs [28,29].…”

Section: Neo4jmentioning

confidence: 99%

“…OrientDB is a multi-model NoSQL database by mixing more than one model. OrientDB main data models are documents and graphs, but it also implements a key-value engine [29]. This NoSQL database uses the free adjacency list to enable native query processing and it uses document database and object-orientation capabilities to store physical vertices.…”

Section: Orientdbmentioning

confidence: 99%

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Consistency Models of NoSQL Databases

2019

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Internet has become so widespread that most popular websites are accessed by hundreds of millions of people on a daily basis. Monolithic architectures, which were frequently used in the past, were mostly composed of traditional relational database management systems, but quickly have become incapable of sustaining high data traffic very common these days. Meanwhile, NoSQL databases have emerged to provide some missing properties in relational databases like the schema-less design, horizontal scaling, and eventual consistency. This paper analyzes and compares the consistency model implementation on five popular NoSQL databases: Redis, Cassandra, MongoDB, Neo4j, and OrientDB. All of which offer at least eventual consistency, and some have the option of supporting strong consistency. However, imposing strong consistency will result in less availability when subject to network partition events.

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Section: Orientdbmentioning

confidence: 98%

“…Neo4j is a graph NoSQL database system. Its data model prioritizes relationships between entities in the form of graphs [28,29].…”

Section: Neo4jmentioning

confidence: 99%

Section: Orientdbmentioning

confidence: 99%

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Consistency Models of NoSQL Databases

2019

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“…Furthermore, they allow to carry out fast transversal queries. Nowadays, there are a variety of software in this field such as ArangoDB (ArangoDB 2019; Fernandes and Bernardino 2018), MongoDB (MongoDB 2019; Fernandes and Bernardino 2018), or Neo4j (Neo4j 2019;Fernandes and Bernardino 2018;Hor et al 2018). We chose to use ArangoDB for its speed carrying out traversal queries, but any other could be used.…”

Section: Implementation Of Graph Databasementioning

confidence: 99%

A semantic graph database for the interoperability of 3D GIS data

et al. 2020

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In the last decades, the use of information management systems in the building data processing led to radical changes to the methods of data production, documentation and archiving. In particular, the possibilities, given by these information systems, to visualize the 3D model and to formulate queries have placed the question of the information sharing in digital format. The integration of information systems represents an efficient solution for defining smart, sustainable and resilient projects, such as conservation and restoration processes, giving the possibilities to combine heterogeneous data. GIS provides a robust data storage system, a definition of topological and semantic relationships and spatial queries. 3D GIS makes possible the creation of threedimensional model in a geospatial context. To promote the interoperability of GIS data, the present research aims first to analyse methods of conversion in CityGML and IndoorGML model, defining an ontological domain. This has led to the creation of a new enriched model, based on connections among the different elements of the urban model in GIS environment, and to the possibility to formulate queries based on these relations. The second step consists in collecting all data translated into a specific format that fill a graph database in a semantic web environment, while maintaining those relationships. The semantic web technology represents an efficient tool of interoperability that leaves open the possibility to import BIM data in the same graph database and to join both GIS and BIM models. The outcome will offer substantial benefits during the entire project life cycle. This methodology can also be applied to cultural heritage where the information management plays a key role.

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“…Connectomics researchers have begun to address these challenges of large-scale graph analysis by adopting existing large-scale graph management software from other domains, such as graph databases, and by enforcing consistent, well-architected data schemas [6,7]. These systems provide performant and cost-effective ways to manipulate larger-than-memory graphs, but tend to require familiarity with complex and nuanced graph query programming languages such as Gremlin or Cypher.…”

Section: Introductionmentioning

confidence: 99%

DotMotif: An open-source tool for connectome subgraph isomorphism search and graph queries

Matelsky

Reilly

Johnson

et al. 2020

Preprint

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As connectomics datasets continue to grow in size and complexity, methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. Recent advances in neuroscience have enabled brain structure exploration at the level of individual synaptic connections. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. This abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly achieve reproducible findings at scale. We demonstrate these tools to search for motifs on simulated data and real public connectomics datasets, and share simple and complex structures relevant to the neuroscience community. We contextualize these results and provide case studies to motivate future neuroscience questions. All of our tools are released open source to empower other scientists to use and extend these methods.

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Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB

Cited by 86 publications

References 0 publications

Consistency Models of NoSQL Databases

Consistency Models of NoSQL Databases

A semantic graph database for the interoperability of 3D GIS data

DotMotif: An open-source tool for connectome subgraph isomorphism search and graph queries

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