The Suitability of Graph Databases for Big Data Analysis: A
Benchmark

D 2020

The Suitability of Graph Databases for Big Data Analysis: A Benchmark

MACÁK, Martin, Matúš ŠTOVČIK and Barbora BÜHNOVÁ

Basic information

Original name

The Suitability of Graph Databases for Big Data Analysis: A Benchmark

Authors

MACÁK, Martin (703 Slovakia, belonging to the institution), Matúš ŠTOVČIK (703 Slovakia, belonging to the institution) and Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution)

Edition

Neuveden, Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, p. 213-220, 8 pp. 2020

Publisher

SciTePress

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Portugal

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14610/20:00115477

Organization unit

Institute of Computer Science

ISBN

978-989-758-426-8

DOI

http://dx.doi.org/10.5220/0009350902130220

UT WoS

000615960700021

Keywords in English

Big Data; Benchmark; Graph Database; Neo4j; PostgreSQL

Abstract

V originále

Digitalization of our society brings various new digital ecosystems (e.g., Smart Cities, Smart Buildings, Smart Mobility), which rely on the collection, storage, and processing of Big Data. One of the recently popular advancements in Big Data storage and processing are the graph databases. A graph database is specialized to handle highly connected data, which can be, for instance, found in the cross-domain setting where various levels of data interconnection take place. Existing works suggest that for data with many relationships, the graph databases perform better than non-graph databases. However, it is not clear where are the borders for specific query types, for which it is still efficient to use a graph database. In this paper, we design and perform tests that examine these borders. We perform the tests in a cluster of three machines so that we explore the database behavior in Big Data scenarios concerning the query. We specifically work with Neo4j as a representative of graph databases and PostgreSQL as a representative of non-graph databases.

Links

EF16_013/0001802, research and development project

Name: CERIT Scientific Cloud

LM2015085, research and development project

Name: CERIT Scientific Cloud (Acronym: CERIT-SC)

Investor: Ministry of Education, Youth and Sports of the CR, CERIT Scientific Cloud

Citovat

MACÁK, Martin, Matúš ŠTOVČIK and Barbora BÜHNOVÁ. The Suitability of Graph Databases for Big Data Analysis: A Benchmark. Online. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS. Neuveden: SciTePress, 2020, p. 213-220. ISBN 978-989-758-426-8. Available from: https://dx.doi.org/10.5220/0009350902130220.

@inproceedings{1642202,
   author = {Macák, Martin and Štovčik, Matúš and Bühnová, Barbora},
   address = {Neuveden},
   booktitle = {Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
   doi = {http://dx.doi.org/10.5220/0009350902130220},
   keywords = {Big Data; Benchmark; Graph Database; Neo4j; PostgreSQL},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Neuveden},
   isbn = {978-989-758-426-8},
   pages = {213-220},
   publisher = {SciTePress},
   title = {The Suitability of Graph Databases for Big Data Analysis: A Benchmark},
   url = {https://www.scitepress.org/PublicationsDetail.aspx?ID=qc6Zz7Qsgn0=&t=1},
   year = {2020}
}

TY  - JOUR
ID  - 1642202
AU  - Macák, Martin - Štovčik, Matúš - Bühnová, Barbora
PY  - 2020
TI  - The Suitability of Graph Databases for Big Data Analysis: A Benchmark
PB  - SciTePress
CY  - Neuveden
SN  - 9789897584268
KW  - Big Data
KW  - Benchmark
KW  - Graph Database
KW  - Neo4j
KW  - PostgreSQL
UR  - https://www.scitepress.org/PublicationsDetail.aspx?ID=qc6Zz7Qsgn0=&t=1
L2  - https://www.scitepress.org/PublicationsDetail.aspx?ID=qc6Zz7Qsgn0=&t=1
N2  - Digitalization of our society brings various new digital ecosystems (e.g., Smart Cities, Smart Buildings, Smart Mobility), which rely on the collection, storage, and processing of Big Data. One of the recently popular advancements in Big Data storage and processing are the graph databases. A graph database is specialized to handle highly connected data, which can be, for instance, found in the cross-domain setting where various levels of data interconnection take place. Existing works suggest that for data with many relationships, the graph databases perform better than non-graph databases. However, it is not clear where are the borders for specific query types, for which it is still efficient to use a graph database. In this paper, we design and perform tests that examine these borders. We perform the tests in a cluster of three machines so that we explore the database behavior in Big Data scenarios concerning the query. We specifically work with Neo4j as a representative of graph databases and PostgreSQL as a representative of non-graph databases.
ER  -

MACÁK, Martin, Matúš ŠTOVČIK and Barbora BÜHNOVÁ. The Suitability of Graph Databases for Big Data Analysis: A Benchmark. Online. In \textit{Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS}. Neuveden: SciTePress, 2020, p.~213-220. ISBN~978-989-758-426-8. Available from: https://dx.doi.org/10.5220/0009350902130220.

Detailed Information on Publication Record