short-paper

Conceptual and Database Modelling of Graph Databases

Author:

Jaroslav PokornýAuthors Info & Claims

IDEAS '16: Proceedings of the 20th International Database Engineering & Applications Symposium

Pages 370 - 377

https://doi.org/10.1145/2938503.2938547

Published: 11 July 2016 Publication History

Abstract

Comparing graph databases with traditional, e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is not explicitly defined, also a conceptual modelling is not used at all. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model and show its relationship to a graph database model. We focus also on integrity constraints modelling functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases and extend them to conditional functional dependencies.

References

[1]

Angels, R. and Gutiérrez, C. 2008. Survey of graph database models. ACM Comput. Surv. 15, 1 (Feb. 2008), DOI=10.1145/1322432.1322433.

Digital Library

[2]

Angels, R. 2012. A Comparison of Current Graph Database Models. In IEEE 28th Int. Conference on Data Engineering Workshops, IEEE, 171-177, DOI=10.1109/ICDEW.2012.31

Digital Library

[3]

Barcello, P. and Fontaine, G. 2015. On the Data Complexity of Consistent Query Answering over Graph Databases. In Proceedings of 18th International Conference on Database Theory (ICDT'15), Leibniz Int. Proceedings in Informatics, 380--397, DOI=10.1145/2274576.2274580

[4]

Barker, R. 1990. CASE*METHODTM: Entity Relationship Modeling. Addison-Wesley Publishing Company, New York, New York.

Digital Library

[5]

Bertossi, L. E. 2011. Database Repairing and Consistent Query Answering. Synthesis Lectures on Data Management. Morgan & Claypool Publishers.

Digital Library

[6]

Calvanese, D., Ortiz, M., and Simkus, M. 2013. Evolving Graph Databases under Description Logic Constraints. In Proceedings of the 26th Int. Workshop on Description Logics (DL 2013). Volume 1014 of CEUR Electronic Workshop Proceedings, http://ceur-ws.org/.

[7]

Ghrab, A., Romero, O., Skhiri, S., and Zimányi, E. 2013. Analytics-Aware Graph Database Modeling. In Proceedings of 15th International Conference, DaWaK 2013, Prague, Czech Republic, August, LNCS 8057, 1--12.

[8]

Ghrab, A., Romero, O., Skhiri, S., Vaisman, A., and Zimányi, E. 2014. GRAD: On Graph Database Modeling. Cornel University Library, arXiv:1602.00503.

[9]

Jadhav, P. and Oberoi, R. 2014. Comparative Analysis of Different Graph Databases. Int. Journal of Engineering Research & Technology, Vol. 3, Issue 9, 820--824.

[10]

Larriba-Pey, J.L., Marínez-Bazán, N., and Doménguez-Sal, D. 2014. Introduction to Graph Databases. In Proceedings of Reasoning Web 2014, LNCS 8714, 171--194.

[11]

Mendelzon, A. O. and Wood, P. T. 1995. Finding regular simple paths in graph databases. Journal SIAM Journal on Computing, 1235--1258. DOI=10.1137/S009753979122370X

Digital Library

[12]

Pokorný, J. 2015. Database technologies in the world of big data. In Proceeding CompSysTech '15 Proceedings of the 16th International Conference on Computer Systems and Technologies, ACM New York, 1--12. DOI=10.1145/2812428.2812429

Digital Library

[13]

Pokorný, J. 2015. Graph Databases: Their Power and Limitations. In Proceedings of 14th Int. Conf. on Computer Information Systems and Industrial Management Applications (CISIM 2015), K. Saeed and W. Homenda (Eds.), LNCS 9339, Springer, 58--69. DOI=10.1007/978-3-319-24369-6_5

[14]

Pokorný, J. and Snášel, V. 2016. Big Graph Storage. In Processing and Visualization. Chapter 12 in Graph Based Social Media Analysis. Chapman and Hall/CRC, I. Pitas (Ed.), 391--416.

[15]

Robinson, I., Webber, J., and Eifrém, E. 2013. Graph Databases. O'Reilly Media.

Digital Library

[16]

Silberschatz, A., Korth, H., abd Sudarshan, S. 2010. Database System Concepts (6th ed.). McGraw-Hill.

[17]

Stardog. http://docs.stardog.com/

[18]

Yu, Y. and Heflin, J. 2011. Extending Functional Dependency to Detect Abnormal Data in RDF Graphs. In SWC 2011, Part I, LNCS 7031, 794--809.

Digital Library

Cited By

Skavantzos PLink S(2025)Entity/Relationship Graphs: Principled Design, Modeling, and Data Integrity Management of Graph DatabasesProceedings of the ACM on Management of Data10.1145/37096903:1(1-26)Online publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1145/3709690
Zhao YWang WChen SZhang BWang JWang Z(2024)Speeding Up Subgraph Matching Queries with Schema Guided IndexProceedings of the 2024 3rd International Conference on Networks, Communications and Information Technology10.1145/3672121.3672129(34-38)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1145/3672121.3672129
Pavliš R(2024)Graph Databases: An Alternative to Relational Databases in an Interconnected Big Data Environment2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569422(247-252)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569422
Show More Cited By

Conceptual and Database Modelling of Graph Databases
1. Information systems
  1. Data management systems
2. Mathematics of computing
  1. Discrete mathematics

Recommendations

Migration of data from relational database to graph database
ICIST '18: Proceedings of the 8th International Conference on Information Systems and Technologies

Relational databases have been widely used in many applications until today and they have met needs for data-intensive domains and transactions, but today data is growing faster than ever and extracting information from this huge data is becoming more ...
Functional querying in graph databases

The paper is focused on a functional querying in graph databases. We consider labelled property graph model and mention also the graph model behind XML databases. An attention is devoted to functional modelling of graph databases both at a conceptual ...
Indexing Patterns in Graph Databases
DATA 2018: Proceedings of the 7th International Conference on Data Science, Technology and Applications

Nowadays graphs have become very popular in domains like social media analytics, healthcare, natural sciences, BI, networking, graph-based bibliographic IR, etc. Graph databases (GDB) allow simple and rapid retrieval of complex graph structures that are ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

IDEAS '16: Proceedings of the 20th International Database Engineering & Applications Symposium

July 2016

420 pages

ISBN:9781450341189

DOI:10.1145/2938503

Editor:
Evan Desai
ConfSys
,
General Chair:
Bipin C. Desai
Concordia University
,
Program Chairs:
Motomichi Toyama
Keio University
,
Jorge Bernardino
ISEC

Copyright © 2016 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

Keio University: Keio University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

IDEAS '16

IDEAS '16: International Database Engineering & Applications Symposium

July 11 - 13, 2016

QC, Montreal, Canada

Acceptance Rates

Overall Acceptance Rate 74 of 210 submissions, 35%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
1,066
Total Downloads

Downloads (Last 12 months)74
Downloads (Last 6 weeks)4

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Skavantzos PLink S(2025)Entity/Relationship Graphs: Principled Design, Modeling, and Data Integrity Management of Graph DatabasesProceedings of the ACM on Management of Data10.1145/37096903:1(1-26)Online publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1145/3709690
Zhao YWang WChen SZhang BWang JWang Z(2024)Speeding Up Subgraph Matching Queries with Schema Guided IndexProceedings of the 2024 3rd International Conference on Networks, Communications and Information Technology10.1145/3672121.3672129(34-38)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1145/3672121.3672129
Pavliš R(2024)Graph Databases: An Alternative to Relational Databases in an Interconnected Big Data Environment2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569422(247-252)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569422
Alabsi MGill ABandara M(2024)Integrated Interaction Journey and Privacy Risk Assessment: A Graph ModelProcedia Computer Science10.1016/j.procs.2024.06.335239(1594-1603)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.335
Šestak MTurkanović M(2023)Extended Property-level k-vertex Cardinality Constraints Model for Graph DatabasesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.03.01335:4(126-138)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.jksuci.2023.03.013
Alsufyani NGill A(2023)A Knowledge-Graph Based Integrated Digital EA Maturity and Performance FrameworkEnterprise Design, Operations, and Computing. EDOC 2022 Workshops10.1007/978-3-031-26886-1_13(214-229)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-3-031-26886-1_13
Santra AKomar KBhowmick SChakravarthy S(2022)From base data to knowledge discovery – A life cycle approach – Using multilayer networksData & Knowledge Engineering10.1016/j.datak.2022.102058141(102058)Online publication date: Sep-2022
https://doi.org/10.1016/j.datak.2022.102058
Darmont JNovikov BWrembel RBellatreche L(2022)Advances on Data Management and Information SystemsInformation Systems Frontiers10.1007/s10796-021-10235-424:1(1-10)Online publication date: 2-Mar-2022
https://doi.org/10.1007/s10796-021-10235-4
Sharma CSinha R(2022)FLASc: a formal algebra for labeled property graph schemaAutomated Software Engineering10.1007/s10515-022-00336-y29:1Online publication date: 2-Apr-2022
https://doi.org/10.1007/s10515-022-00336-y
Tanveer ASharma CSinha RKuo M(2022)Tracing security requirements in industrial control systems using graph databasesSoftware and Systems Modeling10.1007/s10270-022-01019-822:3(851-870)Online publication date: 12-Jul-2022
https://doi.org/10.1007/s10270-022-01019-8
Show More Cited By

View Options

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.

Figures

Tables

Media

View Table of Conten