Experimental Comparison of Graph Databases

Kolomičenko, Vojtěch; Svoboda, Martin; Mlýnková, Irena

doi:10.1145/2539150.2539155

Cited by 26 publications

(13 citation statements)

References 8 publications

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“…Therefore, in the graph option, the neighbourhood search time depends on the number of vertices neighbours. In the relational option, however, on the number of all edges [15].…”

Section: Graph Databasesmentioning

confidence: 99%

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Data Model for Bigdeepexaminator

Bobulski

Kubanek

2019

5th International Conference on Data Mining and Applications (DMAP 2019)

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Big Data is a term used for such data sets, which at the same time are characterized by high volume, diversity, real-time stream inflow, variability, complexity, as well as require the use of innovative technologies, tools and methods in order to extracting new and useful knowledge from them. Big Data is a new challenge and information possibilities. The effective acquisition and processing of data will play a key role in the global and local economy as well as social policy and large corporations. The article is a continuation of research and development works on the design of the data analysis system using artificial intelligence, in which we present a data model for this system.

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“…Therefore, in the graph option, the neighbourhood search time depends on the number of vertices neighbours. In the relational option, however, on the number of all edges [15].…”

Section: Graph Databasesmentioning

confidence: 99%

“…This is mainly about poor performance of graph structure storage in RBD. This is difficult, unnatural and inefficient for RBD [15].…”

Section: Graph Databasesmentioning

confidence: 99%

Data Model for Bigdeepexaminator

Bobulski

Kubanek

2019

5th International Conference on Data Mining and Applications (DMAP 2019)

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“…For example, Titan 6 will retrieve an adjacent node of the edge to be retrieved and then execute a node query to identify the edge. The former is constant time but the latter is potentially linear in the number of edges incident on the node with the same edge label.…”

Section: Graph Queryingmentioning

confidence: 99%

“…Now the most popular book is rather practically oriented work [15]. A performance comparison of some graph databases is presented, e.g., in [6], [9].…”

Section: Introductionmentioning

confidence: 99%

Graph Databases: Their Power and Limitations

Pokorný

2015

Lecture Notes in Computer Science

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Part 1: Full Keynote and Invited PapersInternational audienceReal world data offers a lot of possibilities to be represented as graphs. As a result we obtain undirected or directed graphs, multigraphs and hypergraphs, labelled or weighted graphs and their variants. A development of graph modelling brings also new approaches, e.g., considering constraints. Processing graphs in a database way can be done in many different ways. Some graphs can be represented as JSON or XML structures and processed by their native database tools. More generally, a graph database is specified as any storage system that provides index-free adjacency, i.e. an explicit graph structure. Graph database technology contains some technological features inherent to traditional databases, e.g. ACID properties and availability. Use cases of graph databases like Neo4j, OrientDB, InfiniteGraph, FlockDB, AllegroGraph, and others, document that graph databases are becoming a common means for any connected data. In Big Data era, important questions are connected with scalability for large graphs as well as scaling for read/write operations. For example, scaling graph data by distributing it in a network is much more difficult than scaling simpler data models and is still a work in progress. Still a challenge is pattern matching in graphs providing, in principle, an arbitrarily complex identity function. Mining complete frequent patterns from graph databases is also challenging since supporting operations are computationally costly. In this paper, we discuss recent advances and limitations in these areas as well as future directions

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“…Os vértices representam as entidades, suas propriedades são representadas pelos atributos e as arestas representam as relações[Leavitt 2010]. Esse armazenamento com os dados estritamente conectados, permite que, ao realizar uma pesquisa através da travessia seja possível percorrer milhões de vértices em questão de segundos[Rodriguez and Neubauer 2010].Existem atualmente diversos sistemas de armazenamento orientado a grafos, com estudos bem abrangentes de suas características[Kolomicenko 2013]. Foram analisados vários fatores, como custo, tempo de busca no grafo e tempo gasto para a construção do grafo.…”

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Integrando banco de dados relacional e orientado a grafos para otimizar consultas com alto grau de indireção

Catarino¹

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Agradeço ao meu orientador, Prof. Dr. João Eduardo Ferreira, não somente pela oportunidade oferecida, mas pela confiança e amizade ao longo destes anos. Os ensinamentos, tanto no meio acadêmico quanto fora dele me acompanharão para sempre. Aos meus pais, Antônio e Maria Hilário, por tudo que fizeram e tem feito por mim, sempre me apoiando e ajudando. Agradeço a minha esposa Clara e ao meu irmão Andrei, por sempre estarem ao meu lado, me escutando e incentivando nesta nova etapa. Agradeço aos amigos de jornada, Bruno, Rafael, Rodrigo, Pedro, Mauro e a todos que contribuíram ao longo destes anos, pois o trabalho entregue não só é possível graças as opiniões, mas pelos conselhos e contribuições de todos. vi vii Resumo CATARINO, M. H. Integrando banco de dados relacional e orientado a grafos para otimizar consultas com alto grau de indireção. 2017.

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Experimental Comparison of Graph Databases

Cited by 26 publications

References 8 publications

Data Model for Bigdeepexaminator

Data Model for Bigdeepexaminator

Graph Databases: Their Power and Limitations

Integrando banco de dados relacional e orientado a grafos para otimizar consultas com alto grau de indireção

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