More Web Proxy on the site http://driver.im/

research-article

Data Modeling and NoSQL Databases - A Systematic Mapping Review

Authors:

Harley Vera-Olivera,

Ruben Cruz Huacarpuma,

Ana Paula Bernardi Da Silva,

Ari Melo Mariano,

Maristela HolandaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 6

Article No.: 116, Pages 1 - 26

https://doi.org/10.1145/3457608

Published: 13 July 2021 Publication History

Abstract

Modeling is one of the most important steps in developing a database. In traditional databases, the Entity Relationship (ER) and Unified Modeling Language (UML) models are widely used. But how are NoSQL databases being modeled? We performed a systematic mapping review to answer three research questions to identify and analyze the levels of representation, models used, and contexts where the modeling process occurred in the main categories of NoSQL databases. We found 54 primary studies where we identified that conceptual and logical levels received more attention than the physical level of representation. The UML, ER, and new notation based on ER and UML were adapted to model NoSQL databases, in the same way, formats such as JSON, XML, and XMI were used to generate schemas through the three levels of representation. New contexts such as benchmark, evaluations, migration, and schema generation were identified, as well as new features to be considered for modeling NoSQL databases, such as the number of records by entities, CRUD operations, and system requirements (availability, consistency, or scalability). Additionally, a coupling and co-citation analysis was carried out to identify relevant works and researchers.

References

[1]

F. Abdelhedi, A. Ait Brahim, F. Atigui, and G. Zurfluh. 2017. MDA-based approach for NoSQL databases modelling. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10440 LNCS (2017), 88–102. https://doi.org/10.1007/978-3-319-64283-3_7

[2]

F. Abdelhedi, A. Ait Brahim, and G. Zurfluh. 2018. Formalizing the mapping of UML conceptual schemas to column-oriented databases. International Journal of Data Warehousing and Mining 14, 3 (2018), 44–68. https://doi.org/10.4018/IJDWM.2018070103

Digital Library

[3]

F. Abdelhedi, A. A. Brahim, F. Atigui, and G. Zurfluh. 2016. Big data and knowledge management: How to implement conceptual models in NoSQL systems’. IC3K 2016 - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management 3 (2016), 235–240.

[4]

F. Abdelhedi, A. A. Brahim, F. Atigui, and G. Zurfluh. 2017. Logical unified modeling for NoSQL databases. ICEIS 2017 - Proceedings of the 19th International Conference on Enterprise Information Systems 1 (2017), 249–256.

[5]

F. Abdelhedi, A. A. Brahim, F. Atigui, and G. Zurfluh. 2018. UMLtoNoSQL: Automatic transformation of conceptual schema to NoSQL databases. Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2017-October (2018), 272–279. https://doi.org/10.1109/AICCSA.2017.76

[6]

S. B. Akintoye, A. B. Bagula, O. E. Isafiade, Y. Djemaiel, and N. Boudriga. 2019. Data model for cloud computing environment. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 275 (2019), 199–215. https://doi.org/10.1007/978-3-030-16042-5_19

[7]

Jacky Akoka, Isabelle Comyn-Wattiau, and Nabil Laoufi. 2017. Research on Big Data–a systematic mapping study. Computer Standards & Interfaces 54 (2017), 105–115.

Digital Library

[8]

R. Angles. 2018. The property graph database model. CEUR Workshop Proceedings 2100 (2018).

[9]

Chaimae Asaad and Karim Baïna. 2018. NoSQL Databases–Seek for a Design Methodology. In International Conference on Model and Data Engineering. Springer, 25–40.

[10]

Paolo Atzeni. 2016. Data Modelling in the NoSQL world: A contradiction?. In Proceedings of the 17th International Conference on Computer Systems and Technologies 2016. ACM, 1–4.

Digital Library

[11]

S. Banerjee and A. Sarkar. 2016. Ontology Driven Meta-Modeling for NoSQL Databases: A Conceptual Perspective. International Journal of Software Engineering and its Applications 10, 12 (2016), 41–64. https://doi.org/10.14257/ijseia.2016.10.12.05

[12]

S. Banerjee and A. Sarkar. 2017. Logical level design of NoSQL databases. IEEE Region 10 Annual International Conference, Proceedings/TENCON (2017), 2360–2365. https://doi.org/10.1109/TENCON.2016.7848452

[13]

D. Bermbach, S. Müller, J. Eberhardt, and S. Tai. 2016. Informed Schema Design for Column Store-Based Database Services. Proceedings - 2015 IEEE 8th International Conference on Service-Oriented Computing and Applications, SOCA 2015 (2016), 163–172. https://doi.org/10.1109/SOCA.2015.29

[14]

Eric Brewer. 2010. A certain freedom: thoughts on the CAP theorem. In Proceedings of the 29th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing. 335.

Digital Library

[15]

Francesca Bugiotti, Luca Cabibbo, Paolo Atzeni, and Riccardo Torlone. 2014. Database design for NoSQL systems. In International Conference on Conceptual Modeling. Springer. 223–231.

[16]

A. Chebotko, A. Kashlev, and S. Lu. 2015. A Big Data Modeling Methodology for Apache Cassandra. Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015 (2015), 238–245.

[17]

A. H. Chillón, S. F. Morales, D. S. Ruiz, and J. G. Molina. 2017. Exploring the visualization of schemas for aggregate-oriented nosql databases?CEUR Workshop Proceedings 1979 (2017), 72–85.

[18]

A. Chiş-Raţiu and R. A. Buchmann. 2018. Design and implementation of a diagrammatic tool for creating RDF graphs. CEUR Workshop Proceedings 2238 (2018), 37–48.

[19]

Alejandro Corbellini, Cristian Mateos, Alejandro Zunino, Daniela Godoy, and Silvia Schiaffino. 2017. Persisting big-data: The NoSQL landscape. Information Systems 63 (2017), 1–23.

[20]

G. Daniel, G. Sunyé, and J. Cabot. 2016. UMLtographDB: Mapping conceptual schemas to graph databases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9974 LNCS (2016), 430–444. https://doi.org/10.1007/978-3-319-46397-1_33

[21]

Ali Davoudian, Liu Chen, and Mengchi Liu. 2018. A survey on NoSQL stores. ACM Computing Surveys (CSUR) 51, 2 (2018), 40.

Digital Library

[22]

A. de la Vega, D. García-Saiz, C. Blanco, M. Zorrilla, and P. Sánchez. 2018. Mortadelo: A model-driven framework for NoSQL database design. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11163 LNCS (2018), 41–57. https://doi.org/10.1007/978-3-030-00856-7_3

[23]

C. De Lima and R. Dos Santos Mello. 2015. A workload-driven logical design approach for NoSQL document databases. In Proceedings of 17th International Conference on Information Integration and Web-Based Applications and Services (iiWAS’15). https://doi.org/10.1145/2837185.2837218

[24]

R. De Virgilio, A. Maccioni, and R. Torlone. 2014. Model-driven design of graph databases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8824 (2014), 172–185.

[25]

Miguel Diogo, Bruno Cabral, and Jorge Bernardino. 2019. Consistency Models of NoSQL Databases. Future Internet 11, 2 (2019), 43.

[26]

David Gil and Il-Yeol Song. 2016. Modeling and management of big data: challenges and opportunities.

[27]

Paola Gómez, Rubby Casallas, and Claudia Roncancio. 2016. Data schema does matter, even in NoSQL systems!. In 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS). IEEE, 1–6.

[28]

Hackolade. 2020. Data modeling tool for NoSQL databases. https://hackolade.com/. (Accessed on 10/10/2020).

[29]

S. Hamouda and Z. Zainol. 2018. Document-Oriented Data Schema for Relational Database Migration to NoSQL. Proceedings - 2017 International Conference on Big Data Innovations and Applications, Innovate-Data 2017 2018-January (2018), 43–50. https://doi.org/10.1109/Innovate-Data.2017.13

[30]

Jing Han, E Haihong, Guan Le, and Jian Du. 2011. Survey on NoSQL database. In 2011 6th international conference on pervasive computing and applications. IEEE, 363–366.

[31]

Robin Hecht and Stefan Jablonski. 2011. NoSQL evaluation: A use case oriented survey. In 2011 International Conference on Cloud and Service Computing. IEEE, 336–341.

Digital Library

[32]

M. Hewasinghage, N.B. Seghouani, and F. Bugiotti. 2018. Modeling strategies for storing data in distributed heterogeneous NoSQL databases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11157 LNCS (2018), 488–496. https://doi.org/10.1007/978-3-030-00847-5_35

[33]

A. A. Imam, S. Basri, R. Ahmad, N. Aziz, and M. T. Gonzalez-Aparicio. 2017. New cardinality notations and styles for modeling NoSQL document-store databases. IEEE Region 10 Annual International Conference, Proceedings/TENCON 2017-December (2017), 2765–2770. https://doi.org/10.1109/TENCON.2017.8228332

[34]

A. A. Imam, S. Basri, R. Ahmad, and María Teresa González Aparicio. 2019. Schema proposition model for NoSQL applications. Recent Trends in Data Science and Soft Computing (2019).

[35]

A. A. Imam, S. Basri, R. Ahmad, J. Watada, and M. T. González-Aparicio. 2018. Automatic schema suggestion model for NoSQL document-stores databases. Journal of Big Data 5, 1 (2018). https://doi.org/10.1186/s40537-018-0156-1

[36]

Abdullahi Abubakar Imam, Shuib Basri, Rohiza Ahmad, Junzo Watada, María Teresa González Aparicio, and Malek Ahmad Almomani. 2018. Data modeling guidelines for NoSQL document-store databases. International Journal of Advanced Computer Science and Applications, 9 (2018).

[37]

K. Kaur and R. Rani. 2013. Modeling and querying data in NoSQL databases. Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013 (2013), 1–7. https://doi.org/10.1109/BigData.2013.6691765

[38]

KDM. 2020. The Kashliev Data Modeler. https://www.datafluent.org/. [Online; accessed 10/10/2020].

[39]

Barbara Kitchenham, Rialette Pretorius, David Budgen, O. Pearl Brereton, Mark Turner, Mahmood Niazi, and Stephen Linkman. 2010. Systematic literature reviews in software engineering–a tertiary study. Information and software technology 52, 8 (2010), 792–805.

[40]

X. Li, Z. Ma, and H. Chen. 2014. QODM: A query-oriented data modeling approach for NoSQL databases. Proceedings - 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014 (2014), 338–345. https://doi.org/10.1109/WARTIA.2014.6976265

[41]

Yan Li, Ping Gu, and Chao Zhang. 2014. Transforming UML class diagrams into HBase based on meta-model. In 2014 International Conference on Information Science, Electronics and Electrical Engineering, Vol. 2. IEEE, 720–724.

[42]

C. Lima and R. S. Mello. 2016. On proposing and evaluating a NoSQL document database logical approach. International Journal of Web Information Systems 12, 4 (2016), 398–417. https://doi.org/10.1108/IJWIS-04-2016-0018

[43]

V. Martins de Sousa and L. M. del Val Cura. 2018. Logical design of graph databases from an entity-relationship conceptual model. ACM International Conference Proceeding Series (2018), 183–189. https://doi.org/10.1145/3282373.3282375

[44]

M. J. Mior. 2014. Automated schema design for NoSQL databases. Proceedings of the ACM SIGMOD International Conference on Management of Data (2014), 41–45. https://doi.org/10.1145/2602622.2602624

Digital Library

[45]

Michael Joseph Mior, Kenneth Salem, Ashraf Aboulnaga, and Rui Liu. 2017. NoSE: Schema design for NoSQL applications. IEEE Transactions on Knowledge and Data Engineering 29, 10 (2017), 2275–2289.

Digital Library

[46]

I. D. Nogueira, M. Romdhane, and J. Darmont. 2018. Modeling data lake metadata with a data vault. ACM International Conference Proceeding Series (2018), 253–261. https://doi.org/10.1145/3216122.3216130

[47]

O. Orel, S. Zakošek, and M. Baranović. 2017. Property oriented relational-to-graph database conversion [Konverzija relacijskih u grafovske baze podataka orijentirana na svojstva]. Automatika 57, 3 (2017), 836–845. https://doi.org/10.7305/automatika.2017.02.1581

[48]

Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. 2015. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology 64 (2015), 1–18.

Digital Library

[49]

J. Pokorný. 2016. Conceptual and database modelling of graph databases. ACM International Conference Proceeding Series 11-13-July-2016 (2016), 370–377. https://doi.org/10.1145/2938503.2938547

Digital Library

[50]

Debora G. Reis, Fabio S. Gasparoni, Maristela Holanda, Marcio Victorino, Marcelo Ladeira, and Edward O Ribeiro. 2018. An evaluation of data model for NoSQL document-based databases. In World Conference on Information Systems and Technologies. Springer, 616–625.

[51]

V. Reniers, D. Van Landuyt, A. Rafique, and W. Joosen. 2018. Schema design support for semi-structured data: Finding the sweet spot between NF and De-NF. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 2018-January (2018), 2921–2930. https://doi.org/10.1109/BigData.2017.8258261

[52]

N. Roy-Hubara, L. Rokach, B. Shapira, and P. Shoval. 2017. Modeling Graph Database Schema. IT Professional 19, 6 (2017), 34–43. https://doi.org/10.1109/MITP.2017.4241458

Digital Library

[53]

N. Roy-Hubara, L. Rokach, B. Shapira, and P. Shoval. 2018. Evaluation of a design method for graph database. Lecture Notes in Business Information Processing 318 (2018), 291–303. https://doi.org/10.1007/978-3-319-91704-7_19

[54]

J. Santisteban and R. Ticona-Herrera. 2018. Modeling a persistent graph. Proceedings of a Special Session - 16th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence, MICAI 2017 (2018), 15–22. https://doi.org/10.1109/MICAI-2017.2017.00011

[55]

M. Y. Santos and C. Costa. 2016. Data models in NoSQL databases for big data contexts. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9714 LNCS (2016), 475–485. https://doi.org/10.1007/978-3-319-40973-3_48

[56]

A. Schram and K. M. Anderson. 2012. MySQL to NoSQL data modeling challenges in supporting scalability. SPLASH’12 - Proceedings of the 2012 ACM Conference on Systems, Programming, and Applications: Software for Humanity (2012), 191–202. https://doi.org/10.1145/2384716.2384773

Digital Library

[57]

M. Sedlmeier and M. Gogolla. 2014. Design and prototypical implementation of an integrated graph-based conceptual data model. Frontiers in Artificial Intelligence and Applications 272 (2014), 376–395. https://doi.org/10.3233/978-1-61499-472-5-376

[58]

K. Shin, C. Hwang, and H. Jung. 2017. NoSQL database design using UML conceptual data model based on peter chen’s framework. International Journal of Applied Engineering Research 12, 5 (2017), 632–636.

[59]

P. Shoval. 2018. A method for modeling a schema for graph databases. Digital Presentation and Preservation of Cultural and Scientific Heritage 8 (2018), 99–104.

[60]

Graeme Simsion and Graham Witt. 2004. Data Modeling Essentials. Elsevier.

[61]

P. Suárez-Otero, M. J. Suárez-Cabal, and J. Tuya. 2018. Leveraging conceptual data models for keeping cassandra database integrity. In Proceedings of the 14th International Conference on Web Information Systems and Technologies (WEBIST’18). 398–403.

[62]

Tamás Vajk, László Deák, Krisztián Fekete, and Gergely Mezei. 2013. Automatic NoSQL schema development: A case study. In Artificial Intelligence and Applications. Actapress, 656–663.

[63]

G. Van Erven, W. Silva, R. Carvalho, and M. Holanda. 2018. GRAPHED: A graph description diagram for graph databases. Advances in Intelligent Systems and Computing 745 (2018), 1141–1151. https://doi.org/10.1007/978-3-319-77703-0_111

[64]

V. Varga, K. T. Jánosi-Rancz, and B. Kálmán. 2016. Conceptual design of document NoSQL database with formal concept analysis. Acta Polytechnica Hungarica 13, 2 (2016), 229–248.

[65]

V. Varga, C. Sǎcǎrea, and A. E. Molnar. 2018. Conceptual Graphs Based Modeling of Semi-structured Data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10872 LNAI (2018), 167–175. https://doi.org/10.1007/978-3-319-91379-7_13

[66]

H. Vera, W. Boaventura, M. Holanda, V. Guimarães, and F. Hondo. 2015. Data modeling for NoSQL document-oriented databases. CEUR Workshop Proceedings 1478 (2015), 129–135.

[67]

Fernán Villa, Francisco Moreno, and Jaime Guzmán. 2018. An Analysis of a Methodology that Transforms the Entity-Relationship Model into a Conceptual Model for a Graph Database. In International Conference for Emerging Technologies in Computing. Springer, 70–83.

[68]

A. Vágner. 2018. Store and visualize EeR in Neo4j. ACM International Conference Proceeding Series (2018). https://doi.org/10.1145/3284557.3284694

Digital Library

[69]

K. M. Yoo, S. Park, and S.-G. Lee. 2014. RDB2Graph: A generic framework for modeling relational databases as graphs. CEUR Workshop Proceedings 1312 (2014), 148–151.

[70]

Z. J. Zhang. 2017. Graph Databases for Knowledge Management. IT Professional 19, 6 (2017), 26–32. https://doi.org/10.1109/MITP.2017.4241463

Digital Library

[71]

Gansen Zhao, Weichai Huang, Shunlin Liang, and Yong Tang. 2013. Modeling MongoDB with relational model. In 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies. IEEE, 115–121.

Digital Library

[72]

M. Zhao, Y. Liu, and P. Zhou. 2016. Towards a systematic approach to graph data modeling: Scenario-based design and experiences. Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE 2016-January (2016), 634–637. https://doi.org/10.18293/SEKE2016-119

Cited By

Soni KSachdeva S(2025)MTable: Visual query interface for browsing and navigation in NoSQL data storesJournal of Computer Languages10.1016/j.cola.2024.10131282(101312)Online publication date: Mar-2025
https://doi.org/10.1016/j.cola.2024.101312
Belefqih SZellou ABerquedich M(2024)Schema Extraction in NoSQL Databases: A Systematic Literature ReviewRecent Advances in Computer Science and Communications10.2174/012666255827343723120406110617:8Online publication date: Nov-2024
https://doi.org/10.2174/0126662558273437231204061106
Mozaffari MDignös AGamper JStörl U(2024)Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and TuningACM Computing Surveys10.1145/366532356:11(1-37)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3665323
Show More Cited By

Index Terms

Data Modeling and NoSQL Databases - A Systematic Mapping Review
1. Information systems
  1. Data management systems
    1. Database design and models

Recommendations

NoSQL databases: MongoDB vs cassandra
C3S2E '13: Proceedings of the International C* Conference on Computer Science and Software Engineering

In the past, relational databases were used in a large scope of applications due to their rich set of features, query capabilities and transaction management. However, they are not able to store and process big data effectively and are not very ...
Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and Tuning
Self-tuning is a feature of autonomic databases that includes the problem of automatic schema design. It aims at providing an optimized schema that increases the overall database performance. While in relational databases automatic schema design focuses ...
A unified metamodel for NoSQL and relational databases
Abstract
The Database field is undergoing significant changes. Although relational systems are still predominant, the interest in NoSQL systems is continuously increasing. In this scenario, polyglot persistence is envisioned as the database ...
Graphical abstract

Display Omitted
Highlights
- Definition of a unified data model to represent NoSQL and relational schemas.
- ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 6

Invited Tutorial

July 2022

799 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3475936

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Copyright © 2021 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2021

Accepted: 01 March 2021

Revised: 01 January 2021

Received: 01 December 2019

Published in CSUR Volume 54, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
1,652
Total Downloads

Downloads (Last 12 months)309
Downloads (Last 6 weeks)40

Reflects downloads up to 17 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Soni KSachdeva S(2025)MTable: Visual query interface for browsing and navigation in NoSQL data storesJournal of Computer Languages10.1016/j.cola.2024.10131282(101312)Online publication date: Mar-2025
https://doi.org/10.1016/j.cola.2024.101312
Belefqih SZellou ABerquedich M(2024)Schema Extraction in NoSQL Databases: A Systematic Literature ReviewRecent Advances in Computer Science and Communications10.2174/012666255827343723120406110617:8Online publication date: Nov-2024
https://doi.org/10.2174/0126662558273437231204061106
Mozaffari MDignös AGamper JStörl U(2024)Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and TuningACM Computing Surveys10.1145/366532356:11(1-37)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3665323
Pantelelis MKalloniatis C(2024)Create, Read, Update, Delete: Implications on Security and Privacy Principles regarding GDPRProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3670898(1-7)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3670898
Sachdeva SVasava J(2024)Plugging and Playing with Variety of Data using Multi-Model Database and Polyglot Persistence2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)10.1109/ICEEICT61591.2024.10718481(1-8)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICEEICT61591.2024.10718481
Wu SMueller T(2024)A user-friendly NoSQL framework for managing agricultural field trial dataScientific Reports10.1038/s41598-024-81609-214:1Online publication date: 30-Nov-2024
https://doi.org/10.1038/s41598-024-81609-2
Rodrigues NMariano ARalha C(2024)Author name disambiguation literature review with consolidated meta-analytic approachInternational Journal on Digital Libraries10.1007/s00799-024-00398-125:4(765-785)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00799-024-00398-1
Beheshti AAlinejad-Rokny HSuero Molina EDi Ieva A(2024)Understanding Big Data in NeurosurgeryComputational Neurosurgery10.1007/978-3-031-64892-2_10(157-175)Online publication date: 11-Nov-2024
https://doi.org/10.1007/978-3-031-64892-2_10
Asaad C(2023)Towards Leveraging Artificial Intelligence for NoSQL Data Modeling, Querying and Quality Characterization2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)10.1109/MODELS-C59198.2023.00047(192-198)Online publication date: 1-Oct-2023
https://doi.org/10.1109/MODELS-C59198.2023.00047
Gounaris AMichailidou ADustdar S(2023)Toward Building Edge Learning PipelinesIEEE Internet Computing10.1109/MIC.2022.317164327:1(61-69)Online publication date: 1-Jan-2023
https://doi.org/10.1109/MIC.2022.3171643
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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents