Creating the Conceptual and Logical Model of a Journal Database

György Hampel

doi:10.14232/analecta.2022.1.110-121

Authors

György Hampel University of Szeged https://orcid.org/0000-0003-0550-1878

DOI:

https://doi.org/10.14232/analecta.2022.1.110-121

Keywords:

database planning, entity-relationship model, relational model

Abstract

This article describes the process of creating the conceptual and logical model of a journal database. To efficiently extract the information from the articles published in the journal so far, the idea of creating a database has emerged. To create a database, it is recommended to design a high-level conceptual model and convert that into a logical data model. The benefit of the thoughtful design is that it shows the structure of the database in an easily comprehensible form. The entity-relationship model is a fast and efficient way to create the conceptual model and it can be easily converted to a relational database model, which is a logical model. The initial version of the entity-relationship model of the journal database had one entity type, 25 attributes, and no relationship. The final version contained three entity types, 39 attributes, and three relationships. This final conceptual model was converted to a logical model, the relational model. The result was ten tables to store entity data with 22 different fields and another three tables to ensure the relationships between the tables. The developed model can be created in a relational database manager and is suitable for serving information needs related to the journal.

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References

Z. Fabulya, Access alkalmazás kialakítása ügyfélközpontú szolgáltatások nyilvántartására, Jelenkori Társadalmi és Gazdasági Folyamatok, 13 (1-2) (2018), pp. 67-76. http://acta.bibl.u-szeged.hu/55838/1/jelenkori_013_001_002_067-076.pdf

Z. Fabulya, Access alkalmazás kialakítása dolgozói jelenlét nyilvántartására, Jelenkori Társadalmi és Gazdasági Folyamatok, 13 (1-2) (2018), pp. 151-160. http://acta.bibl.u-szeged.hu/62062/1/jelenkori_013_003_004_151-160.pdf

H. Garcia-Milina, J. D. Ullman, J. Widom, Database Systems: The Complete Book, 2nd Edition, Prentice Hall, Upper Saddle River, New Jersey, 2008

L. Tímár, K. Vígh, J. Tátrai, J. Szigeti, Á. Vathy, É. Telekesi, I. Vass, T. Kocsis, R. Zs. Priskinné, M. Erdélyiné, Építsünk könnyen és lassan adatmodellt! Veszprémi Egyetem & Műszertechnika-Veszprém Kft., Veszprém, 1997

B. Halassy, Az adatbázis-tervezés alapjai és titkai, IDG Magyarországi Lapkiadó Kft., Budapest, 1994

L. Kovács, Adatbázisok tervezésének és kezelésének módszertana, Computer Books, Budapest, 2004

D. M. Kroenke, C. D. Gray, Toward a Next Generation Data Modeling Facility: Neither the Entity-Relationship Model nor UML Meet the Need, Journal of Information Systems Education, 17 (1) (2006), pp. 29-38.

B. L. Kacsukné, T. Kiss, Bevezetés az üzleti informatikába, Akadémiai Kiadó, Budapest, 2019 https://doi.org/10.1556/9789634544852

Gy. Hampel, Cs. Heves, Informatika alapjai mérnököknek, alapszakos hallgatók számára, Szegedi Tudományegyetem, Szeged, 2019

V. T. N. Chau, S. Chittayasothorn, "A Bitemporal SQL Database Design Method from the Enhanced Entity-Relationship Model," 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2021, pp. 85-90, http://www.doi.org/10.1109/ICEAST52143.2021.9426270

D. Dey, V. Storey, B. Terence, Improving Database Design Through the Analysis of Relationships, ACM Transactions on Database Systems, 24 (4) (1999), pp. 453-486. https://doi.org/10.1145/331983.331984

R. T. Watson, The Essential Skills of Data Modeling, Journal of Information Systems Education, 17 (1) (2006), pp. 39-41. https://aisel.aisnet.org/jise/vol17/iss1/7/

A. Badia, D. Lemire, A Call to Arms: Revisiting Database Design, Sigmod Record, 40 (3) (2011), pp. 61-69. https://doi.org/10.1145/2070736.2070750

B. Halassy, Adatmodellezés. Elmélet és gyakorlat, Budapest, 2000, https://mek.oszk.hu/11100/11144

R. Foorthuis, On the nature and types of anomalies: a review of deviations in data, International Journal of Data Science and Analytics, 12 (2021), pp. 297-331. https://doi.org/10.1007/s41060-021-00265-1

P. P.-Sh. Chen, The Entity-Relationship Model – Toward a Unified View of Data, ACM Transactions on Database Systems, 1 (1) (1976), pp. 9-36. https://doi.org/10.1145/320434.320440

S. Hartmann, Reasoning about participation constraints and Chen's constraints, Database Technologies 2003, Proceedings of the 14th Australasian Database Conference, ADC 2003, Adelaide, South Australia, February 2003, pp. 105-113. https://www.researchgate.net/publication/221152543_Reasoning_about_participation_constraints_and_Chen%27s_constraints

T. A. Carte, J. Jasperson, M. E. Cornelius, Integrating ERD and UML Concepts When Teaching Data Modeling, Journal of Information Systems Education, 17 (1) (2006), pp. 55-63. https://aisel.aisnet.org/jise/vol17/iss1/9/

Ch. L. Dunn, G. J. Gerard, S. V. Grabski, Critical Evaluation of Conceptual Data Models, International Journal of Accounting Information Systems, 6 (2) (2005), pp. 83-106. https://doi.org/10.1016/j.accinf.2004.03.002

J. Dullea, Il-Y. Song, I. Lamprou, An Analysis of Structural Validity in Entity-Relationship modeling, Data & Knowledge Engineering, 47 (2) (2003), pp. 167-205. https://doi.org/10.1016/S0169-023X(03)00049-1

C. E. H. Chua, V. C. Storey, Issues and Guidelines in Modeling Decomposition of Minimum Participation in Entity-Relationship Diagrams, Communications of the Association for Information Systems, 29 (9) (2011), pp. 159-184. https://doi.org/10.17705/1CAIS.02909

Nyerges T., Logical Data Models, The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2017 Edition), John P. Wilson (ed.). 2017. https://doi.org/10.22224/gistbok/2017.1.2

A. Silberschatz, H. F. Korth, S. Sudarshan, Data Models, ACM Computing Surveys, 28 (1) (1996) pp. 105-108. https://doi.org/10.1145/234313.234360

B. Szabó, Adatbázis fejlesztés és üzemeltetés I., Eszterházy Károly Főiskola, Eger, 2013

E. F. Codd, Derivability, Redundancy, and Consistency of Relations Stored in Large Data Banks, Research Report RJ599, IBM, San Jose, California, 1969

M. Russo, A. Ferrari, Analyzing Data with Power BI and Power Pivot for Excel, Microsoft Press, Redmond, Washington, 2017

A. Silberschatz, H. F. Korth, S. Sudarshan, Database System Concepts, 7th editon, McGraw-Hill Education, New York, 2020