Modelling of Graph Databases

Tóm tắt

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 mostly not explicitly defined, also a conceptual modelling is not used. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined or only their very simple representatives can be specified. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. We focus also on integrity constraints modelling and propose functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases. We show a number of examples of often cited GDBMSs and their approach to database schemas and ICs specification. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model based on a binary variant of the ER model and show its relationship to a graph database model, i.e. a mapping conceptual schemas to database schemas. An alternative based on the conceptual functions called attributes is presented.

Từ khoá

Attribute, graph conceptual modelling, graph database, graph database modelling.

Tài liệu tham khảo

[1] LARRIBA-PEY, J. L., N. MARTINEZBAZAN
and D. DOMINGUEZ-SAL. Introduction
to Graph Databases. In: 10th International
Summer School. Athens: Springer,
2014, pp. 171194. ISBN 978-3-319-10586-
4. ISSN 0302-9743.
[2] GHRAB, A., O. ROMERO, S. SKHIRI,
A. VAISMAN, and E. ZIMANYI. BGRAD:
On Graph Database Modelling. In: Cornel
University Library [online]. 2016.
[3] ANGLES, R. A Comparison of Current
Graph Database Models. In: 28th International
Conference on Data Engineering
Workshops. Arlington: IEEE, 2012,
pp. 171177. ISBN 978-0-7695-4748-0.
[4] ROBINSON, I., J. WEBBER and E.
EIFREM. Graph databases. 1st ed.
O'Reilly Media, 2013. ISBN 978-1-4493-
5626-2.
[5] POKORNY, Jaroslav and Vaclav SNASEL.
Graph-based social media analysis: In
Graph Based Social Media Analysis. Chapman
and Hall/CRC, 2015, pp. 391416.
Chapman. ISBN 978-1-4987-1904-9.
[6] POKORNY, J. Graph Databases: Their
Power and Limitations. In: Proceedings of
14th Int. Conf. on Computer Information
Systems and Industrial Management Applications
(CISIM 2015). vol. 9339. Warsaw:
Springer, 2015, pp. 5869.
[7] POKORNY, J. Conceptual and Database
Modelling of Graph Databases. In:
Proceedings of the 20th International
Database Engineering. Montreal: ACM
Press, 2016, pp. 370377. ISBN 978-1-
4503-4118-9.
[8] JADHAV, P. and R. OBEROI. Comparative
Analysis of Dierent Graph Databases.
Int. Journal of Engineering Research &
Technology, vol. 3, iss. 9, pp. 820824, 2014.
ISSN 2278-0181.
[9] DELFOSSE, V., R. BILLEN and P.
LECLERCQ. UML as a schema candidate
for graph databases. In: Proceedings of
NoSQL Matters. 2012, pp. 18.
[10] POKORNY, J. Functional Querying in
Graph Databases. In: Asian Conference
on Intelligent Information and Database
Systems. Kanazawa: Springer, 2017,
vol. 10191. pp. 291301. ISBN 978-3-319-
54471-7.
[11] MENDELZON, A. O. and P. T. WOOD.
Finding Regular Simple Paths in Graph
Databases. SIAM Journal on Computing.
1995, vol. 24. no. 6, pp. 12351258.
[12] POKORNY, J. A function: unifying mechanism
for entity-oriented database models.
In: Entity-Relationship Approach: A
Bridge to the User, Proceedings of the Seventh
International Conference on EntityRelationship
Approach, North-Holland: Elsevier
Science Publishers B.V., pp. 165181,
1989.
[13] KAUR, K. and R. RANI. Modeling and
querying data in NoSQL databases. In: International
Conference on Big Data. Silicon
Valley: IEEE, 2013, pp. 17. ISBN 978-1-
4799-1293-3.
[14] ANGLES, R. and C. GUTIERREZ. Survey
of graph database models. ACM Computing
Surveys (CSUR). 2008, vol. 4, iss. 1. ISSN
0360-0300.
[15] CALVANESE, D., M. OORTIZ, and M.
SIMKUS. Evolving Graph Databases under
Description Logic Constraints. In: Proceedings
of the 26th Int. Workshop on Description
Logics (DL 2013). 2013, vol. 1014,
[16] YU, Y. and J. HEFLIN. Extending Functional
Dependency to Detect Abnormal
Data in RDF Graphs. In: The Semantic
Web  ISWC. 2013, Berlin: Springer,
vol. 7031, pp. 794809. ISBN 978-3-642-
25072-9.
[17] SILBERSCHATZ, A., H. KORTH, and S.
SUDARSHAN, Database System Concepts.
6th ed. McGraw-Hill, 2010.
[18] BARCELLO, P. and G. FONTAINE, 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, pp. 380
397, 2015. ISSN 0022-0000.
[19] BARKER, B., CASE*METHOD: Entity
Relationship Modeling. Addison-Wesley
Publishing Company, New York, 1990.
[20] POKORNY, J. Database semantics in heterogeneous
environment. In: Proceedings of
23rd Seminar SOFSEM 96: Theory and
Practice of Informatics, Springer-Verlag,
pp. 125-142, 1996.
[21] Titan: Distributed Graph Database [online].
2017. Available at: http://titan.
thinkaurelius.com/.
[22] OrientDB Ltd. OrientDB [online]. 2017.
Available at: http://orientdb.com/.
[23] DBengines. DB-Engines Ranking of
Graph DBMS [online]. 2017. Available
at: https://db-engines.com/en/
ranking/graph+dbms.
[24] Stardog Union. Stardog 5 [online]. 2017.
Available at: http://www.stardog.
com/.
[25] Sparcity Technologies. Scalable highperformance
graph database [online].
2017. Available at: http:
//sparsity-technologies.com/
#sparksee.
[26] Objectivity. InniteGraph [online].
2017. Available at: http://www.
objectivity.com/products/
infinitegraph/#.U8O_yXnm9I0.