Multi-model Database Design and Query Processing in NewSQL DBMSs

Joachim Tankoano

doi:doi:10.11648/j.ajcst.20250802.12

Research Article |

| Peer-Reviewed

Multi-model Database Design and Query Processing in NewSQL DBMSs

Joachim Tankoano^*

Published in American Journal of Computer Science and Technology (Volume 8, Issue 2)

Received: 4 April 2025 Accepted: 17 April 2025 Published: 9 May 2025

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

NewSQL DBMSs are hybrid systems that combine the advantages of both SQL DBMSs and NoSQL DBMSs. This paper proposes a method for designing a database to be implemented on a NewSQL DBMS. The objectives of this method are identifying and defining heterogeneous collections of values that adhere to different data models, which are essential for an enterprise's operations, with the goal of storing and managing them within only one database. This method is based on a Relational Data Store and the Nested Relational Model. It allows the designer to use the Data Store as a guarantor of integrity and to optimize it for hybrid workloads (transactional and analytical), performance, scalability, and continuous data availability. As for the nested relational model, it is used by the designer to: (1) clarify their choices regarding storage models that can enable fast access to data about complex real-world entities; (2) specify access paths that can meet user needs. The main interest and the originality of this methodological approach are that this enables us to use the Nested Relational Model as a Pivot Model to: (1) automatically generate the global external schemas of the NoSQL virtual databases, allowing users to view and manipulate the Data Store as if it were a NoSQL database (object-relational, XML, JSON, or graph-oriented), and (2) unify the processing of cross-model SQL queries through an innovative and efficient approach. This method consistently integrates, through five levels of abstraction, the design process of the relational Data Store and that of the virtual databases. The research method used consisted of: (1) defining the objectives of this approach, (2) identifying the required levels of abstraction in light of the targeted objectives, (3) determining, for each level of abstraction, its specific objectives as well as the role to be played by the designer, a design support tool, and the DBMS, and (4) applying this approach to a typical example reflecting the most common needs, in order to facilitate the understanding of its contributions and relevance with respect to the intended objectives.

Published in	American Journal of Computer Science and Technology (Volume 8, Issue 2)
DOI	10.11648/j.ajcst.20250802.12
Page(s)	22-41
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Multi-model Data, NewSQL, NoSQL, Database Design, Query Processing

References

[1]	Qingsong Guo1, Chao Zhang, Shuxun Zhang, Jiaheng Lu. Multi‑model query languages: taming the variety of big data. Distributed and Parallel Databases (2024) 42: 31–71.
[2]	Jiaheng Lu and Irena Holubova. Multi-model Databases: A New Journey to Handle the Variety of Data. ACM Computing Surveys. 2019, Vol. 0, No. 0.
[3]	Jan Michels, Keith Hare, Krishna Kulkarni, Calisto Zuzarte, Zhen Hua Liu, Beda Hammerschmidt, Fred Zemke. The New and Improved SQL: 2016 Standard. SIGMOD Record. June 2018 (Vol. 47, No. 2).
[4]	Kian Win Ong, Yannis Papakonstantinou, Romain Vernoux. The SQL++ Unifying Semi-structured Query Language, and an Expressiveness Benchmark of SQL-on-Hadoop, NoSQL and NewSQL Databases. arXiv: 1405.3631 [cs.DB], Dec. 2015.
[5]	Manjunatha Sughaturu Krishnappa, Bindu Mohan Harve, Vivekananda Jayaram, Akshay Nagpal, Koushik Kumar Ganeeb, Balaji Shesharao Ingole. ORACLE 19C Sharding: A Comprehensive Guide to Modern Data Distribution. IJCET. Sep-Oct 2024, Volume 15, Issue 5.
[6]	Samuel Akinola. Trends in Open Source RDBMS: Performance, Scalability and Security Insights. Journal of Research in Science and Engineering (JRSE). July 2024, Volume-6, Issue-7.
[7]	Naresh Kumar Miryala. Emerging Trends and Challenges in Modern Database Technologies: A Comprehensive Analysis. International Journal of Science and Research (IJSR). November 2024, Volume 13 Issue 11.
[8]	Abdullah Muhammed, Zul Hilmi Abdullah, Waidah Ismail1, Ali Y. Aldailamy, Abduljalil Radman, Rimuljo Hendradi, Radhi Rafiee Afandi. A Survey of NewSQL DBMSs focusing on Taxonomy, Comparison and Open Issues. IJCSMC. December 2021. Volume 11, Issue 4.
[9]	Tariq N. Khasawneh, Mahmoud Alsahlee, Ali Safieh. SQL, NewSQL, and NOSQL Databases: A Comparative Survey. In 2020 11th International Conference on Information and Communication Systems (ICICS)
[10]	María Murazzo1, Pablo Gómez, Nelson Rodríguez, Diego Medel. Database NewSQL Performance Evaluation for Big Data in the Public Cloud. In Book Communications in Computer and Information Science ((CCIS, volume 1050)), Naiouf, M., Chichizola, F., Rucci, E. (eds) Cloud Computing and Big Data. JCC&BD 2019.
[11]	Andrew Pavlo, Matthew Aslett. What’s Really New with NewSQL? SIGMOD Record. June 2016 (Vol. 45, No. 2).
[12]	Maia, Francisco Carlos M. B. Oliveira. Sharding by Hash Partitioning A database scalability pattern to achieve evenly sharded database clusters. 17th ICEIS At: Barcelona, Spain, April 2015.
[13]	A. Moniruzzaman. NewSQL: Towards Next-Generation Scalable RDBMS for Online Transaction Processing (OLTP) for Big Data Management. arXiv preprint arXiv: 1411.7343, 2014.
[14]	Joachim Tankoano. Providing in RDBMSs the flexibility to Work with Various Non-Relational Data Models. Global Journal of Computer Science and Technology: H Information & Technology. 2023, Volume 23 Issue 2 Version 1.0. https://doi.org/10.34257/GJCSTHVOL23IS2PG1
[15]	Chen, P., P-S. The entity-relationship model - toward a unified view of data. ACM TODS. March 1976, Volume 1, Issue 1. pp 9–36.
[16]	Object Modeling Group. Unified Modeling Language Specification. October 2012, Version 2.5.
[17]	Patrick Valduriez, Setrag Khoshajian, George Copeland. Implementation Techniques of Complex Objects. 12th Int. Conference on Very Large Data Bases - Kyoto, August 1986.
[18]	Tirthankar LahiriSerge, Abiteboul Serge, Jennifer Widom. Ozone: Integrating Structured and Semistructured Data. 7th Int. Workshop on Database Programming Languages: Research Issues in Structured and Semi-structured Database Programming, December 1999.
[19]	Marc H. Scholl. Extensions to the Relational Data Model. Available from: https://www.researchgate.net/publication/2381217_Extensions_to_the_Relational_Data_Model (accessed 29 March 2025).
[20]	Joachim Tankoano. Modèle relationnel imbriqué. In SGBD relationnels – Tome 2, Vers les Bases de données Réparties, Objet, Objet-relationnelles, XML, … Available from: https://www.researchgate.net/publication/366548683_SGBD_relationnels_-_Tome_2_Vers_les_Bases_de_donnees_Reparties_Objet_Objet-relationnelles_XML (accessed 29 March 2025).
[21]	Z. Meral Ozsoyoglu, Li-Yan Yuan. On the normalization in Nested Relational Databases. LNCS. 1989, volume 361.
[22]	Daniel J. Abadi, Samuel R. Madden, Nabil Hachem. Column-Stores vs. Row-Stores: How Different Are They Really? SIGMOD'08, June 9–12, 2008, Vancouver, BC, Canada.
[23]	ORACLE. Oracle Database SQL Language. Reference 23ai, F47038-19, November 2024.
[24]	Comer, D. The Ubiquitous B-Tree. Computing Surveys. June 1979, vol. 11, n° 2.
[25]	Valduriez P. Join Indices. ACM TODS. June 1987, Vol. 12, No. 2, Pages 218-246.
[26]	Ashish P. Mohod, Manoj S. Chaudhari. Improve Query Performance Using Effective Materialized View Selection and Maintenance: A Survey. IJCSMC. April 2013, Vol. 2, Issue. 4, pg. 485 – 490.
[27]	International Organization for Standardization (ISO). Information technology — Database languages SQL Part 16: Property Graph Queries (SQL/PGQ). (Edition 1, 2023), ISO/IEC 9075-16: 2023.
[28]	Caio H. Costa, João Vianney B. M. Filho, Paulo Henrique M. Yunkai Lou, Longbin Lai, Bingqing Lyu, Yufan Yang, Xiaoli Zhou, Wenyuan Yu, Ying Zhang, Jingren Zhou. Towards a Converged Relational-Graph Optimization Framework. Proc. ACM Manag. Data, Vol. 2, No. 6 (SIGMOD), December 2024.
[29]	Ronald Fagin, Phokion G. Kolaitis, Alan Nash. Towards a Theory of Schema-Mapping Optimization”. PODS’08, June 9–12, 2008, Vancouver, BC, Canada.
[30]	Jean Bézivin, Olivier Gerbé: Towards a precise definition of the OMG/MDA framework. Proc. 16th Annual Int. Conf. on Automated Software Engineering (ASE 2001)
[31]	Chaimae Asaad and Karim Ba. NoSQL Databases: Seek for a Design Methodology. 8th Int. Conference, MEDI 2018, Marrakesh, Morocco, October 24–26, 2018.
[32]	Carlyna Bondiombouy, Patrick Valduriez. Query Processing in Multistore Systems: an overview. [Research Report] RR-8890, INRIA Sophia Antipolis - Méditerranée. 2016, pp. 38. hal-01289759v2.
[33]	Paolo Atzeni, Francesca Bugiotti, and Luca Rossi. Uniform Access to Non-relational Database Systems: The SOS Platform. J. Ralyt´e et al. (Eds.): CAiSE 2012, LNCS 7328, pp. 160–174, 2012.
[34]	Ágnes Vathy-Fogarassy, Tamás Hugyák. Uniform data access platform for SQL and NoSQL database systems. Information Systems. September 2017, Volume 69, Pages 93-105.
[35]	Kwangchul Shin, Chulhyun Hwang, Hoekyung Jung. NoSQL Database Design Using UML Conceptual Data Model Based on Peter Chen’s Framework. Int. Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 5 (2017) pp. 632-636.
[36]	Fatma Abdelhedi, Amal Ait Brahim, Faten Atigui, Gilles Zurfluh. Logical Unified Modeling For NoSQL DataBases. 19th ICEIS 2017, Apr 2017, Porto, Portugal. pp. 249-256. hal-01782574.
[37]	Michael Stonebraker. NoSQL and Enterprises. Cacm \| august 2011 \| vol. 54 \| no.

Cite This Article

Plain Text BibTeX RIS

APA Style

Tankoano, J. (2025). Multi-model Database Design and Query Processing in NewSQL DBMSs. American Journal of Computer Science and Technology, 8(2), 22-41. https://doi.org/10.11648/j.ajcst.20250802.12

Copy | Download

ACS Style

Tankoano, J. Multi-model Database Design and Query Processing in NewSQL DBMSs. Am. J. Comput. Sci. Technol. 2025, 8(2), 22-41. doi: 10.11648/j.ajcst.20250802.12

Copy | Download

AMA Style

Tankoano J. Multi-model Database Design and Query Processing in NewSQL DBMSs. Am J Comput Sci Technol. 2025;8(2):22-41. doi: 10.11648/j.ajcst.20250802.12

Copy | Download

@article{10.11648/j.ajcst.20250802.12,
  author = {Joachim Tankoano},
  title = {Multi-model Database Design and Query Processing in NewSQL DBMSs
},
  journal = {American Journal of Computer Science and Technology},
  volume = {8},
  number = {2},
  pages = {22-41},
  doi = {10.11648/j.ajcst.20250802.12},
  url = {https://doi.org/10.11648/j.ajcst.20250802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20250802.12},
  abstract = {NewSQL DBMSs are hybrid systems that combine the advantages of both SQL DBMSs and NoSQL DBMSs. This paper proposes a method for designing a database to be implemented on a NewSQL DBMS. The objectives of this method are identifying and defining heterogeneous collections of values that adhere to different data models, which are essential for an enterprise's operations, with the goal of storing and managing them within only one database. This method is based on a Relational Data Store and the Nested Relational Model. It allows the designer to use the Data Store as a guarantor of integrity and to optimize it for hybrid workloads (transactional and analytical), performance, scalability, and continuous data availability. As for the nested relational model, it is used by the designer to: (1) clarify their choices regarding storage models that can enable fast access to data about complex real-world entities; (2) specify access paths that can meet user needs. The main interest and the originality of this methodological approach are that this enables us to use the Nested Relational Model as a Pivot Model to: (1) automatically generate the global external schemas of the NoSQL virtual databases, allowing users to view and manipulate the Data Store as if it were a NoSQL database (object-relational, XML, JSON, or graph-oriented), and (2) unify the processing of cross-model SQL queries through an innovative and efficient approach. This method consistently integrates, through five levels of abstraction, the design process of the relational Data Store and that of the virtual databases. The research method used consisted of: (1) defining the objectives of this approach, (2) identifying the required levels of abstraction in light of the targeted objectives, (3) determining, for each level of abstraction, its specific objectives as well as the role to be played by the designer, a design support tool, and the DBMS, and (4) applying this approach to a typical example reflecting the most common needs, in order to facilitate the understanding of its contributions and relevance with respect to the intended objectives.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Multi-model Database Design and Query Processing in NewSQL DBMSs

AU  - Joachim Tankoano
Y1  - 2025/05/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajcst.20250802.12
DO  - 10.11648/j.ajcst.20250802.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 22
EP  - 41
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20250802.12
AB  - NewSQL DBMSs are hybrid systems that combine the advantages of both SQL DBMSs and NoSQL DBMSs. This paper proposes a method for designing a database to be implemented on a NewSQL DBMS. The objectives of this method are identifying and defining heterogeneous collections of values that adhere to different data models, which are essential for an enterprise's operations, with the goal of storing and managing them within only one database. This method is based on a Relational Data Store and the Nested Relational Model. It allows the designer to use the Data Store as a guarantor of integrity and to optimize it for hybrid workloads (transactional and analytical), performance, scalability, and continuous data availability. As for the nested relational model, it is used by the designer to: (1) clarify their choices regarding storage models that can enable fast access to data about complex real-world entities; (2) specify access paths that can meet user needs. The main interest and the originality of this methodological approach are that this enables us to use the Nested Relational Model as a Pivot Model to: (1) automatically generate the global external schemas of the NoSQL virtual databases, allowing users to view and manipulate the Data Store as if it were a NoSQL database (object-relational, XML, JSON, or graph-oriented), and (2) unify the processing of cross-model SQL queries through an innovative and efficient approach. This method consistently integrates, through five levels of abstraction, the design process of the relational Data Store and that of the virtual databases. The research method used consisted of: (1) defining the objectives of this approach, (2) identifying the required levels of abstraction in light of the targeted objectives, (3) determining, for each level of abstraction, its specific objectives as well as the role to be played by the designer, a design support tool, and the DBMS, and (4) applying this approach to a typical example reflecting the most common needs, in order to facilitate the understanding of its contributions and relevance with respect to the intended objectives.

VL  - 8
IS  - 2
ER  -

Copy | Download

Author Information

Joachim Tankoano

Institut Burkinabè des Arts et des Métiers, Université Joseph Ki-Zerbo, Ouagadougou, Burkina Faso

Contact Email

http://orcid.org/0000-0003-1245-6235

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Tankoano, J. (2025). Multi-model Database Design and Query Processing in NewSQL DBMSs. American Journal of Computer Science and Technology, 8(2), 22-41. https://doi.org/10.11648/j.ajcst.20250802.12

Copy | Download

ACS Style

Tankoano, J. Multi-model Database Design and Query Processing in NewSQL DBMSs. Am. J. Comput. Sci. Technol. 2025, 8(2), 22-41. doi: 10.11648/j.ajcst.20250802.12

Copy | Download

AMA Style

Tankoano J. Multi-model Database Design and Query Processing in NewSQL DBMSs. Am J Comput Sci Technol. 2025;8(2):22-41. doi: 10.11648/j.ajcst.20250802.12

Copy | Download

@article{10.11648/j.ajcst.20250802.12,
  author = {Joachim Tankoano},
  title = {Multi-model Database Design and Query Processing in NewSQL DBMSs
},
  journal = {American Journal of Computer Science and Technology},
  volume = {8},
  number = {2},
  pages = {22-41},
  doi = {10.11648/j.ajcst.20250802.12},
  url = {https://doi.org/10.11648/j.ajcst.20250802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20250802.12},
  abstract = {NewSQL DBMSs are hybrid systems that combine the advantages of both SQL DBMSs and NoSQL DBMSs. This paper proposes a method for designing a database to be implemented on a NewSQL DBMS. The objectives of this method are identifying and defining heterogeneous collections of values that adhere to different data models, which are essential for an enterprise's operations, with the goal of storing and managing them within only one database. This method is based on a Relational Data Store and the Nested Relational Model. It allows the designer to use the Data Store as a guarantor of integrity and to optimize it for hybrid workloads (transactional and analytical), performance, scalability, and continuous data availability. As for the nested relational model, it is used by the designer to: (1) clarify their choices regarding storage models that can enable fast access to data about complex real-world entities; (2) specify access paths that can meet user needs. The main interest and the originality of this methodological approach are that this enables us to use the Nested Relational Model as a Pivot Model to: (1) automatically generate the global external schemas of the NoSQL virtual databases, allowing users to view and manipulate the Data Store as if it were a NoSQL database (object-relational, XML, JSON, or graph-oriented), and (2) unify the processing of cross-model SQL queries through an innovative and efficient approach. This method consistently integrates, through five levels of abstraction, the design process of the relational Data Store and that of the virtual databases. The research method used consisted of: (1) defining the objectives of this approach, (2) identifying the required levels of abstraction in light of the targeted objectives, (3) determining, for each level of abstraction, its specific objectives as well as the role to be played by the designer, a design support tool, and the DBMS, and (4) applying this approach to a typical example reflecting the most common needs, in order to facilitate the understanding of its contributions and relevance with respect to the intended objectives.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Multi-model Database Design and Query Processing in NewSQL DBMSs

AU  - Joachim Tankoano
Y1  - 2025/05/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajcst.20250802.12
DO  - 10.11648/j.ajcst.20250802.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 22
EP  - 41
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20250802.12
AB  - NewSQL DBMSs are hybrid systems that combine the advantages of both SQL DBMSs and NoSQL DBMSs. This paper proposes a method for designing a database to be implemented on a NewSQL DBMS. The objectives of this method are identifying and defining heterogeneous collections of values that adhere to different data models, which are essential for an enterprise's operations, with the goal of storing and managing them within only one database. This method is based on a Relational Data Store and the Nested Relational Model. It allows the designer to use the Data Store as a guarantor of integrity and to optimize it for hybrid workloads (transactional and analytical), performance, scalability, and continuous data availability. As for the nested relational model, it is used by the designer to: (1) clarify their choices regarding storage models that can enable fast access to data about complex real-world entities; (2) specify access paths that can meet user needs. The main interest and the originality of this methodological approach are that this enables us to use the Nested Relational Model as a Pivot Model to: (1) automatically generate the global external schemas of the NoSQL virtual databases, allowing users to view and manipulate the Data Store as if it were a NoSQL database (object-relational, XML, JSON, or graph-oriented), and (2) unify the processing of cross-model SQL queries through an innovative and efficient approach. This method consistently integrates, through five levels of abstraction, the design process of the relational Data Store and that of the virtual databases. The research method used consisted of: (1) defining the objectives of this approach, (2) identifying the required levels of abstraction in light of the targeted objectives, (3) determining, for each level of abstraction, its specific objectives as well as the role to be played by the designer, a design support tool, and the DBMS, and (4) applying this approach to a typical example reflecting the most common needs, in order to facilitate the understanding of its contributions and relevance with respect to the intended objectives.

VL  - 8
IS  - 2
ER  -

Copy | Download