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 a Barbora BÜHNOVÁ

Základní údaje

Originální název

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

Autoři

MACÁK, Martin (703 Slovensko, domácí), Matúš ŠTOVČIK (703 Slovensko, domácí) a Barbora BÜHNOVÁ (203 Česká republika, domácí)

Vydání

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

Nakladatel

SciTePress

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14610/20:00115477

Organizační jednotka

Ústav výpočetní techniky

ISBN

978-989-758-426-8

DOI

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

UT WoS

000615960700021

Klíčová slova anglicky

Big Data; Benchmark; Graph Database; Neo4j; PostgreSQL

Štítky

firank_B, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 27. 3. 2021 15:55, RNDr. Martin Macák, Ph.D.

Anotace

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.

Návaznosti

EF16_013/0001802, projekt VaV

Název: CERIT Scientific Cloud

LM2015085, projekt VaV

Název: CERIT Scientific Cloud (Akronym: CERIT-SC)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CERIT Scientific Cloud

Citovat

MACÁK, Martin, Matúš ŠTOVČIK a 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, s. 213-220. ISBN 978-989-758-426-8. Dostupné z: 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 a 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, s.~213-220. ISBN~978-989-758-426-8. Dostupné z: https://dx.doi.org/10.5220/0009350902130220.

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