research-article

Experimental Comparison of Graph Databases

Authors:

Vojtěch Kolomičenko,

Martin Svoboda,

Irena Holubová MlýnkováAuthors Info & Claims

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

Pages 115 - 124

https://doi.org/10.1145/2539150.2539155

Published: 02 December 2013 Publication History

Abstract

In the recent years a new type of NoSQL databases, called graph databases (GDBs), has gained significant popularity due to the increasing need of processing and storing data in the form of a graph. The objective of this paper is a research on possibilities and limitations of GDBs and conducting an experimental comparison of selected GDB implementations. For this purpose the requirements of a universal GDB benchmark have been formulated and an extensible benchmarking tool, called BlueBench, has been developed.

References

[1]

Dominguez-Sal, Urbón-Bayes, Giménez-Vanó, Gómez-Villamor, Martínez-Bazán, Larriba-Pey. Survey of Graph Database Performance on the HPC Scalable Graph Analysis Benchmark. Springer Berlin Heidelberg, 2010, Pages 37--48. ISBN 978-3-642-16720-1.

Digital Library

[2]

Bader, Feo, Gilbert, Kepner, Koester, Loh, Madduri, Mann, Meuse, Robinson. HPC Scalable Graph Analysis Benchmark. 2009. (http://www.graphanalysis.org/benchmark/index.html)

[3]

Chakrabarti, Zhan, Faloutsos. R-MAT: A Recursive Model for Graph Mining. 2004. (http://repository.cmu.edu/compsci/541/)

[4]

Ciglan, Averbuch, Hluchy. Benchmarking traversal operations over graph databases. 2012. (http://ups.savba.sk/~marek/papers/gdm12-ciglan.pdf)

[5]

Lancichinetti, Fortunato. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80, 016118, 2009. (https://sites.google.com/site/andrealancichinetti/benchmark2.pdf?attredirects=0)

[6]

Vicknair, Macias, Zhao, Nan, Chen, Wilkins. A comparison of a graph database and a relational database: a data provenance perspective. 2010, ACM SE '10, Article No. 42. ISBN: 978-1-4503-0064-3.

Digital Library

[7]

Ciglan. SGDB3 -- Simple Graph Database. (http://ups.savba.sk/~marek/sgdb.html)

[8]

TinkerPop. Blueprints -- Graph Indices. (https://github.com/tinkerpop/blueprints/wiki/Graph-Indices)

[9]

sparsity technologies. Why DEX. (http://www.sparsity-technologies.com/dex)

[10]

DB-Engines. Ranking of Graph DBMS. (http://db-engines.com/en/ranking/graph+dbms)

[11]

sparsity technologies. DEX -- A High-Performance Graph Database Management System. (http://www.sparsity-technologies.com/dex)

[12]

Angles Renzo. Say hi to GraphDB-Bench. 2012. (http://dcc.utalca.cl/~rangles/files/gdm2012.pdf)

[13]

Robinson, Webber, Eifrem. Graph Databases. 2013, O'Reilly Media. (http://graphdatabases.com/)

Digital Library

[14]

Objectivity inc. Understanding Accelerated Ingest. (http://wiki.InfiniteGraph.com/3.0/w/index.php?title=Understanding_Accelerated_Ingest)

[15]

Redmond, Wilson. Seven Databases in Seven Weeks. 2012, O'Reilly Media. ISBN: 978-1-934356-92-0. (http://it-ebooks.info/book/866/)

[16]

NuvolaBase Ltd. OrientDB. (https://github.com/nuvolabase/orientdb)

[17]

NuvolaBase Ltd. OrientDB -- Concepts -- Record Version. (https://github.com/nuvolabase/orientdb/wiki/Concepts#record-version)

[18]

NuvolaBase Ltd. OrientDB -- Transactions. (https://github.com/nuvolabase/orientdb/wiki/Transactions)

[19]

Aurelius. Titan. (https://github.com/thinkaurelius/titan/wiki)

[20]

Aurelius. Titan -- Indexing Backend Overview. (https://github.com/thinkaurelius/titan/wiki/Indexing-Backend-Overview)

[21]

Aurelius. Titan -- Storage Backend Overview. (https://github.com/thinkaurelius/titan/wiki/Storage-Backend-Overview)

[22]

Broecheler Matthias. Big Graph Data. (http://www.slideshare.net/knowfrominfo/big-graph-data)

[23]

Rodriguez Marko. The Rise of Big Graph Data. (http://www.slideshare.net/slidarko/titan-the-rise-of-big-graph-data)

[24]

Erdos, Renyi. On random graphs. Mathematicae 6, 1959, Pages 290--297.

[25]

Leskovec, Lang, Dasgupta, Mahoney. Statistical properties of community structure in large social and information networks. ACM Press 2008. Pages 695--704. ISBN. 978-1-60558-085-2.

Digital Library

[26]

Barabási, Albert. Emergence of scaling in random networks. Science. 2008. Vol. 286, no. 5439. Pages 509--512.

Cited By

Sen RTian Y(2023)Microarchitectural Analysis of Graph BI Queries on RDBMSProceedings of the 19th International Workshop on Data Management on New Hardware10.1145/3592980.3595321(102-106)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3592980.3595321
Tian Y(2023)The World of Graph Databases from An Industry PerspectiveACM SIGMOD Record10.1145/3582302.358232051:4(60-67)Online publication date: 25-Jan-2023
https://dl.acm.org/doi/10.1145/3582302.3582320
Asiler MYazıcı AGeorge R(2022)HyGraph: a subgraph isomorphism algorithm for efficiently querying big graph databasesJournal of Big Data10.1186/s40537-022-00589-09:1Online publication date: 21-Apr-2022
https://doi.org/10.1186/s40537-022-00589-0
Show More Cited By

Index Terms

Experimental Comparison of Graph Databases
1. Applied computing
  1. Document management and text processing
    1. Document management
      1. Text editing

Recommendations

Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB
DATA 2018: Proceedings of the 7th International Conference on Data Science, Technology and Applications

Graph databases are a very powerful solution for storing and searching for data designed for data rich in relationships, such as Facebook and Twitter. With data multiplication and data type diversity there has been a need to create new storage and ...
Logical Design of Graph Databases from an Entity-Relationship Conceptual Model
iiWAS2018: Proceedings of the 20th International Conference on Information Integration and Web-based Applications & Services

NoSQL database models and data store systems have emerged in response to the needs of new big data applications. A common feature of these logical models is the schemaless property that promotes flexibility in data organization. Because of that ...
Graph DBs vs. Column-Oriented Stores: A Pure Performance Comparison
ALGOCLOUD 2015: Revised Selected Papers of the First International Workshop on Algorithmic Aspects of Cloud Computing - Volume 9511

Cloud Computing has brought a great change in the way information is stored and applications run. In order for one or more clusters to work as a cloud we need a middleware framework, such as Apache Hadoop [17], that provides reliability, scalability and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

December 2013

753 pages

ISBN:9781450321136

DOI:10.1145/2539150

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

@WAS: International Organization of Information Integration and Web-based Applications and Services

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

IIWAS '13

IIWAS '13: The 15th International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2013

Vienna, Austria

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
947
Total Downloads

Downloads (Last 12 months)19
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sen RTian Y(2023)Microarchitectural Analysis of Graph BI Queries on RDBMSProceedings of the 19th International Workshop on Data Management on New Hardware10.1145/3592980.3595321(102-106)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3592980.3595321
Tian Y(2023)The World of Graph Databases from An Industry PerspectiveACM SIGMOD Record10.1145/3582302.358232051:4(60-67)Online publication date: 25-Jan-2023
https://dl.acm.org/doi/10.1145/3582302.3582320
Asiler MYazıcı AGeorge R(2022)HyGraph: a subgraph isomorphism algorithm for efficiently querying big graph databasesJournal of Big Data10.1186/s40537-022-00589-09:1Online publication date: 21-Apr-2022
https://doi.org/10.1186/s40537-022-00589-0
Prusti DDas DRath S(2021)Credit Card Fraud Detection Technique by Applying Graph Database ModelArabian Journal for Science and Engineering10.1007/s13369-021-05682-9Online publication date: 4-May-2021
https://doi.org/10.1007/s13369-021-05682-9
Stergiopoulos GDedousis PGritzalis D(2021)Automatic analysis of attack graphs for risk mitigation and prioritization on large-scale and complex networks in Industry 4.0International Journal of Information Security10.1007/s10207-020-00533-4Online publication date: 27-Feb-2021
https://doi.org/10.1007/s10207-020-00533-4
Mondal SMukherjee N(2021)Efficient NoSQL Graph Database for Storage and Access of Health DataComputer Communication, Networking and IoT10.1007/978-981-16-0980-0_14(135-146)Online publication date: 19-Jun-2021
https://doi.org/10.1007/978-981-16-0980-0_14
Tian YXu EZhao WPirahesh MTong SSun WKolanko TApu MPeng HMaier DPottinger RDoan ATan WAlawini ANgo H(2020)IBM Db2 Graph: Supporting Synergistic and Retrofittable Graph Queries Inside IBM Db2Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3386138(345-359)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3386138
Wang RYang ZZhang WLin X(2020)An Empirical Study on Recent Graph Database SystemsKnowledge Science, Engineering and Management10.1007/978-3-030-55130-8_29(328-340)Online publication date: 20-Aug-2020
https://doi.org/10.1007/978-3-030-55130-8_29
Lepuschitz WMayerhofer MHoebert TMerdan M(2019)Integration of 3D-Printing Processes with a Cloud Manufacturing Platform2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)10.1109/ISIE.2019.8781103(1650-1655)Online publication date: Jun-2019
https://doi.org/10.1109/ISIE.2019.8781103
Yerashenia NBolotov A(2019)Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology2019 IEEE 21st Conference on Business Informatics (CBI)10.1109/CBI.2019.00017(84-93)Online publication date: Jul-2019
https://doi.org/10.1109/CBI.2019.00017
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents