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Artykuły w czasopismach na temat „Relational databases”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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Tengeri, Dávid, and Ferenc Havasi. "Database Slicing on Relational Databases." Acta Cybernetica 21, no. 4 (2014): 629–53. http://dx.doi.org/10.14232/actacyb.21.4.2014.6.

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Maatuk, Abdelsalam, M. Akhtar Ali, and Nick Rossiter. "Converting Relational Databases into Object-relational Databases." Journal of Object Technology 9, no. 2 (2010): 145. http://dx.doi.org/10.5381/jot.2010.9.2.a3.

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Sharma, Yashraj, and Yashasvi Sharma. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research -GRANTHAALAYAH 7, no. 7 (July 31, 2019): 351–59. http://dx.doi.org/10.29121/granthaalayah.v7.i7.2019.777.

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On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also some features of relational database management system. The severe benefit of NoSQL database is that it is an open source system which helps to adapt many numbers of features for newly generated applications. This paper is focused on understanding the concepts of non-relational database system architecture with relational database system architecture and figure out the advantages and disadvantages of both simultaneously.

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Yashraj, Sharma, and Sharma Yashasvi. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research - Granthaalayah 7, no. 7 (July 31, 2019): 351–59. https://doi.org/10.5281/zenodo.3364448.

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On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also some features of relational database management system. The severe benefit of NoSQL database is that it is an open source system which helps to adapt many numbers of features for newly generated applications. This paper is focused on understanding the concepts of non-relational database system architecture with relational database system architecture and figure out the advantages and disadvantages of both simultaneously.

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Finkelstein, S., M. Schkolnick, and P. Tiberio. "Physical database design for relational databases." ACM Transactions on Database Systems 13, no. 1 (March 1988): 91–128. http://dx.doi.org/10.1145/42201.42205.

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Garvey, M. "Relational databases." Information and Software Technology 34, no. 12 (December 1992): 825. http://dx.doi.org/10.1016/0950-5849(92)90125-9.

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NAVNEET KUMAR, KASHYAP, PANDEY B.K, MANDORIA H.L, and KUMAR ASHOK. "A REVIEW OF LEADING DATABASES: RELATIONAL and NON-RELATIONAL DATABASE." i-manager's Journal on Information Technology 5, no. 2 (2016): 34. http://dx.doi.org/10.26634/jit.5.2.6002.

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Thakur, Nimesh, and Nishi Gupta. "Relational and Non Relational Databases: A Review." Journal of University of Shanghai for Science and Technology 23, no. 08 (August 4, 2021): 117–21. http://dx.doi.org/10.51201/jusst/21/08341.

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Relational and non-relational databases are the two types of databases that are used to store data and perform dierent operations on it. For data storage, they use a variety of formats. In this paper, we’ll try to gure out what they’re all about and what the main dierences are. Databases serve as a data centre from which information is collected and processed. Data science is a multidisciplinary eld that combines mathematics, statistics, and programming to research data. For a data scientist, a basic understanding of databases is a must-have ability. We’ll look at how a data scientist can make the most of dierent database types.

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Sliusarenko, Tetiana, and Valentin Filatov. "RELATIONAL VS NON-RELATIONAL DATABASES." Grail of Science, no. 23 (January 4, 2023): 269–71. http://dx.doi.org/10.36074/grail-of-science.23.12.2022.41.

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In this paper we’re going to talk about the difference between relational and non-relational databases. These are two different ways in which clients store the data that they have and operationalize it. And we know there is so much data that is coming into every single company today that it’s important that customers have options for how they want to store that data.

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Baliński, Patryk, Łukasz Chudy, and Maria Skublewska-Paszkowska. "Comparative analysis of the performance of relational and non-relational databases in applications implemented in C#." Journal of Computer Sciences Institute 34 (March 30, 2025): 44–53. https://doi.org/10.35784/jcsi.6688.

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The article focuses on comparing relational and non-relational databases using applications written in C#. The aim of the study is to understand in which cases relational databases are preferred and when it is worth considering the use of non-relational databases. The research examines the speed of data retrieval, updating, and deletion, in the context of five different databases, including relational ones like PostgreSQL, MySql, Oracle, and non-relational ones such as Neo4j and MongoDB. The data consists of 1,578,098 records. In the case of relational databases, a unified database model was applied, while in NoSQL databases, the data model was appropriately adjusted to the specific type of non-relational database. Differences in the execution time of database queries are analyzed based on indexing strategies and query complexity. Special attention is given to query performance efficiency in the context of using C# and libraries that facilitate the connection between the application and databases. The conducted research indicates that the PostgreSQL database achieves the lowest average query response times.

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., Vinay Goyal. "REENGINEERING OF RELATIONAL DATABASES TO OBJECTORIENTED DATABASE." International Journal of Research in Engineering and Technology 03, no. 01 (January 25, 2014): 112–15. http://dx.doi.org/10.15623/ijret.2014.0301018.

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Nisa, Behjat U. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 845–48. http://dx.doi.org/10.31142/ijtsrd11214.

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Bulyha, Kostiantin, Olena Bulyha, and Mykola Huzii. "Visualization of Relational Databases." Digital Platform: Information Technologies in Sociocultural Sphere 5, no. 1 (June 30, 2022): 83–89. https://doi.org/10.31866/2617-796X.5.1.2022.261292.

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The purpose of the article is to demonstrate the relational databases visualization algorithm using MS Power BI software products in MS Excel. The research methodology is cloud information processing technologies. The novelty of the study is the implementation of the relational database visualization algorithm using MS Power BI software products in MS Excel. Conclusions. Using MS Power Query and MS Power Pivot software in MS Excel allows you to present visually summary information from databases. The example of connecting these products to the database management system (DBMS) MS Acces shows the ability to visualize the database using summary tables and charts.

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Alekseev, Konstantin. "Relational database problems." Кибернетика и программирование, no. 2 (February 2020): 7–18. http://dx.doi.org/10.25136/2644-5522.2020.2.34076.

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The relevance of this article lies in the fact that today's databases are the basis of numerous information systems.&nbsp;The information accumulated in them is extremely valuable material, and today database processing methods are widely spread in terms of extracting additional methods, knowledge from them, which are interconnected with generalization and various additional methods of information processing.The object of research in this work is relational databases and DBMS, the subject of research is the features of their use in applied programming.In accordance with the set goal, it is necessary to solve the following tasks:1) to consider the concept and essence of a relational database;2) to analyze the problematic aspects of relational databases in modern conditions. Relational databases are among the most widespread due to their simplicity and clarity at the creation stage and at the user level. It should also be noted that the main advantage of RDB is its compatibility with the main query language SQL, which is intuitive for users.Nevertheless, with all the variety of approaches, there are still some canons, violation of which greatly affects both the design of the database and its operation. For example, the problem of database normalization is very relevant. Neglecting normalization makes the database structure confusing and the database itself unreliable.Promising directions include the development of queries to a relational database using heuristic methods, as well as the method of accumulating previously optimized queries with subsequent verification of the derivability of the current query from the accumulated ones.Finally, a very slow decline in relational databases is probably happening. While they are still the primary storage medium, especially in large enterprise projects, they are gradually being replaced by non-relational solutions that will become the majority over time.

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K.Rathva, Mayuree, and Sahani G.J. "Watermarking Relational Databases." International Journal of Computer Science, Engineering and Applications 3, no. 1 (February 28, 2013): 71–79. http://dx.doi.org/10.5121/ijcsea.2013.3107.

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Yang, Xiaoyan, Cecilia M. Procopiuc, and Divesh Srivastava. "Summarizing relational databases." Proceedings of the VLDB Endowment 2, no. 1 (August 2009): 634–45. http://dx.doi.org/10.14778/1687627.1687699.

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Seltzer, Margo. "Beyond Relational Databases." Queue 3, no. 3 (April 2005): 50–58. http://dx.doi.org/10.1145/1059791.1059807.

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Seltzer, Margo. "Beyond relational databases." Communications of the ACM 51, no. 7 (July 2008): 52–58. http://dx.doi.org/10.1145/1364782.1364797.

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Jackson, MS. "Beyond relational databases." Information and Software Technology 32, no. 4 (May 1990): 258–65. http://dx.doi.org/10.1016/0950-5849(90)90059-z.

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Reznichenko, V. A. "60 Years of Databases." PROBLEMS IN PROGRAMMING, no. 3 (September 2021): 040–71. http://dx.doi.org/10.15407/pp2021.03.040.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of the era of relational databases, the results of E. Codd's scientific activities, the theory of dependencies and normal forms, query languages, experimental research and development, optimization and standardization, and transaction management are revealed. The extended relational databases phase is devoted to describing temporal, spatial, deductive, active, object, distributed and statistical databases, array databases, and database machines and data warehouses. At the next stage, the problems of post-relational databases are disclosed, namely, NOSQL-, NewSQL- and ontological databases. The sixth stage is devoted to the disclosure of the causes of occurrence, characteristic properties, classification, principles of work, methods and technologies of big data. Finally, the last section provides a brief overview of database research and development in the Soviet Union.

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Nakhare, Disha. "A Comparative study of SQL Databases and NoSQL Databases for E-Commerce." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 409–12. http://dx.doi.org/10.22214/ijraset.2021.39263.

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Abstract: With the advent of E-Commerce, businesses persistently examine various ways to improvise and accomplish their demands with web engineering that provide notable resolution. The progress in economic status demands colossal databases that store the data efficiently. The databases currently used are relational or non-relational. Both these types have their benefits and limitations that influence the overall processing of data. Non-relational databases are referred to as NoSQL-not only SQL, and Relational databases are known as SQL-Structured Query Language. It has been suggested in many studies that NoSQL databases surpass SQL databases. Our paper aims to evaluate these claims by analyzing the CRUD [Create, Read, Update, Delete] operations executed by both database types. Keywords: NoSQL, SQL, Non-relational Databases, MySQL, E-Commerce, MongoDb , Relational Databases

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Reznichenko, V. A. "60 Years of Databases (part three)." PROBLEMS IN PROGRAMMING, no. 1 (March 2022): 034–66. http://dx.doi.org/10.15407/pp2022.01.034.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of the era of relational databases, the results of E. Codd’s scientific activities, the theory of dependencies and normal forms, query languages, experimental research and development, optimization and standardization, and transaction management are revealed. The extended relational databases phase is devoted to describing temporal, spatial, deductive, active, object, distributed and statistical databases, array databases, and database machines and data warehouses. At the next stage, the problems of post-relational databases are disclosed, namely, NOSQL-, NewSQL- and ontological databases. The sixth stage is devoted to the disclosure of the causes of occurrence, characteristic properties, classification, principles of work, methods and technologies of big data. Finally, the last section provides a brief overview of database research and development in the Soviet Union.

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Reznichenko, V. A. "60 Years of Databases (part two)." PROBLEMS IN PROGRAMMING, no. 4 (December 2021): 036–61. http://dx.doi.org/10.15407/pp2021.04.036.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of the era of relational databases, the results of E. Codd’s scientific activities, the theory of dependencies and normal forms, query languages, experimental research and development, optimization and standardization, and transaction management are revealed. The extended relational databases phase is devoted to describing temporal, spatial, deductive, active, object, distributed and statistical databases, array databases, and database machines and data warehouses. At the next stage, the problems of post-relational databases are disclosed, namely, NOSQL-, NewSQL- and ontological databases. The sixth stage is devoted to the disclosure of the causes of occurrence, characteristic properties, classification, principles of work, methods and technologies of big data. Finally, the last section provides a brief overview of database research and development in the Soviet Union.

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Reznichenko, V. A. "60 Years of Databases (final part)." PROBLEMS IN PROGRAMMING, no. 1 (January 2023): 66–103. http://dx.doi.org/10.15407/pp2023.01.066.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/ SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of the era of relational databases, the results of E. Codd’s scientific activities, the theory of dependencies and normal forms, query languages, experimental research and development, optimization and standardization, and transaction management are revealed. The extended relational databases phase is devoted to describing temporal, spatial, deductive, active, object, distributed and statistical databases, array databases, and database machines and data warehouses. At the next stage, the problems of post-relational databases are disclosed, namely, NOSQL-, NewSQL- and ontological databases. The sixth stage is devoted to the disclosure of the causes of occurrence, characteristic properties, classification, principles of work, methods and technologies of big data. Finally, the last section provides a brief overview of database research and development in the Soviet Union.

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Behjat, U. Nisa. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development 2, no. 3 (April 20, 2018): 845–48. https://doi.org/10.31142/ijtsrd11214.

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Databases are used for storing and managing large amounts of data. Relational model is useful when it comes to reliability but when it comes to the modern applications dealing with large amounts of data and the data is unstructured non relational models are usable. NoSQL databases are used to store large amounts of data. NoSQL databases are non relational, distributed, open source and are horizontally scalable. This paper provides the comparison of the relational model with NoSQL Behjat U Nisa "A Comparison between Relational Databases and NoSQL Databases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: https://www.ijtsrd.com/papers/ijtsrd11214.pdf

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Bordoloi, Subhrajyoti, and Bichitra Kalita. "Designing Graph Database Models from Existing Relational Databases." International Journal of Computer Applications 74, no. 1 (July 26, 2013): 25–31. http://dx.doi.org/10.5120/12850-9303.

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Qadah and Irani. "A Database Machine for Very Large Relational Databases." IEEE Transactions on Computers C-34, no. 11 (November 1985): 1015–25. http://dx.doi.org/10.1109/tc.1985.1676534.

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Reshma, K.R, K.R Reshma, and Mariam Varghese Surekha. "OUTCOME ANALYSIS IN ACADEMIC INSTITUTIONS USING NEO4J." International Journal of Computational Science and Information Technology (IJCSITY) 4, MAY (May 31, 2016): 1–10. https://doi.org/10.5281/zenodo.3463026.

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ABSTRACT Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work aims to investigate and compare the performance and scalability of relational databases to graph databases in terms of handling multilevel queries such as finding the impact of a particular subject with the working area of pass out students. MySQL was chosen as the relational database, Neo4j as the graph database. KEYWORDS Neo4j, NOSQL, Graph database

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Zmaranda, Doina R., Cristian I. Moisi, Cornelia A. Győrödi, Robert Ş. Győrödi, and Livia Bandici. "An Analysis of the Performance and Configuration Features of MySQL Document Store and Elasticsearch as an Alternative Backend in a Data Replication Solution." Applied Sciences 11, no. 24 (December 7, 2021): 11590. http://dx.doi.org/10.3390/app112411590.

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In recent years, with the increase in the volume and complexity of data, choosing a suitable database for storing huge amounts of data is not easy, because it must consider aspects such as manageability, scalability, and extensibility. Nowadays, the NoSQL databases have gained immense popularity for their efficiency in managing such datasets compared to relational databases. However, relational databases also exhibit some advantages in certain circumstances, therefore many applications use a combined approach: relational and non-relational. This paper performs a comparative evaluation of two popular open-source DBMSs: MySQL Document Store and Elasticsearch as non-relational DBMSs; this comparison is based on a detailed analysis of CRUD operations for different amounts of data showing how the databases could be modeled and used in an application. A case-study application was developed for this purpose in Java programming language and Spring framework using for data storage both relational MySQL and non-relational Elasticsearch and MySQL Document Store. To model the real situation encountered in several developed applications that use both relational and non-relational databases, a data replication solution that imports data from the primary relational MySQL database into Elasticsearch and MySQL Document Store as possible alternatives for more efficient data search was proposed and implemented.

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Keivani, Negin, Abdelsalam M. Maatuk, Shadi Aljawarneh, and Muhammad Akhtar Ali. "Towards the Maturity of Object-Relational Database Technology: Promises and Reality." International Journal of Technology Diffusion 6, no. 4 (October 2015): 1–19. http://dx.doi.org/10.4018/ijtd.2015100101.

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Object-relational technology provides a significant increase in scalability and flexibility over the traditional relational databases. The additional object-relational features are particularly satisfying for advanced database applications that relational database systems have experienced difficulties. The key factor to the success of object-relational database systems is their performance. This paper aims to review the promises of Object-Relational database systems, examine the reality, and how their promises may be fulfilled through unification with the relational technology. To investigate the performance implications of using object-relational relative to relational technology, the query-oriented BUCKY benchmark has been previously applied to an early object-relational database system, i.e., Illustra 97. This paper presents the results obtained from implementing and running the BUCKY benchmark on Oracle 10g. The results acquired from the work described in this paper are compared with the results obtained in BUCKY benchmark. This study throws light on the functionality of object-relational databases, where object-relational technology has made improvements but some limitations are identified as well. In general, the performance of relational supersedes that of object-relational database system.

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Zhou, Peng, Mei Li, Jing Huang, and Hua Fang. "Research on Database Schema Comparison of Relational Databases and Key-Value Stores." Advanced Materials Research 1049-1050 (October 2014): 1860–63. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1860.

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With the rapid development of Internet technology, the management capacity of traditional relational databases becomes relatively inefficient when facing the access and processing of big data. As a kind of non-relational databases, the key-value stores, with its high scalability, provide an efficient solution to the problem. This article introduces the concept and features of Key-Value stores, and followed by the comparison with the traditional relational databases, and an example is illustrated to explain its typical application and finally the existing problems of Key-Value stores are summarized.

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Reshma, K.R, Femy P.F Mary, and Mariam Varghese Surekha. "OUTCOME ANALYSIS IN ACADEMIC INSTITUTIONS USING NEO4J." International Journal of Computational Science and Information Technology (IJCSITY) 4, no. 2 (May 31, 2016): 1–10. https://doi.org/10.5281/zenodo.3698858.

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ABSTRACT Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work aims to investigate and compare the performance and scalability of relational databases to graph databases in terms of handling multilevel queries such as finding the impact of a particular subject with the working area of pass out students. MySQL was chosen as the relational database, Neo4j as the graph database. KEYWORDS Neo4j, NOSQL, Graph database

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Kunda, Douglas, and Hazael Phiri. "A Comparative Study of NoSQL and Relational Database." Zambia ICT Journal 1, no. 1 (December 11, 2017): 1–4. http://dx.doi.org/10.33260/zictjournal.v1i1.8.

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Relational Database and NoSQL are competing types of database models. The former has been in existence since 1979 and the latter since the year 2000. The demands of modern applications especially in web 2.0, 3.0 and big data have made NoSQL a popular database of choice. Choosing an appropriate database model to use is an important decision that developers must make based on the features of a given database model. This paper compares the features of Relational Databases and NoSQL to establish which database is better at supporting demands of modern applications. The paper also brings out the challenges of NoSQL. Finally, the paper concludes by determining whether Relational Databases would completely be replaced by NoSQL database models. The findings revealed that, Relational Databases are based on ACID model which emphasizes better consistency, security and offers a standard query language. However, Relational Databases have poor scalability, weak performance, cost more, face availability challenges when supporting large number of users and handle limited volume of data. NoSQL, on the other hand is based on the BASE model, which emphasizes greater scalability and provides a flexible schema, offers better performance, mostly open source, cheap but, lacks a standard query language and does not provide adequate security mechanisms. Both databases will continue to exist alongside each other with none being better than the other. The choice of the database to use will depend on the nature of the application being developed. Each database type has its own challenges and strengths, with relational database lacking of support for unstructured data while NoSQL lacks standardization and has poor security. Modern applications in web 2.0, 3.0 and big data are well suited to use NoSQL but, there are still many applications that rely on Relational Databases.

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Chung, Jen-Yao, Yi-Jing Lin, and Daniel T. Chang. "Object and relational databases." ACM SIGPLAN OOPS Messenger 6, no. 4 (October 1995): 164–69. http://dx.doi.org/10.1145/260111.260273.

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Llorens, J., and A. Trénor. "MARC and relational databases." Electronic Library 11, no. 2 (February 1993): 93–96. http://dx.doi.org/10.1108/eb045213.

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Kocharekar, Raju. "Nulls in relational databases." ACM SIGMOD Record 18, no. 1 (March 1989): 68–73. http://dx.doi.org/10.1145/382272.382416.

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Wei, Ling Ling, and Wei Yang. "A Constructed Method of the Hash Function for the Rough Relational Databases." Applied Mechanics and Materials 427-429 (September 2013): 2588–91. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2588.

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Rough relational database model provided a processing method for uncertainty data, So based on the Hash technology and the data characteristic of rough relational database, it was researched the multiple value data item in the rough relational database represented by binary string in virtue of equivalence classes, calculated its decimal value, and constructed Hash Function. Then according to the decimal number distributed Hash address where stored the data of the rough relational databases. Finally, an algorithm for constructed method of Hash function for the rough relational databases was described and illustrated by an example,and proved the method is validity and practicability.

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Reznichenko, V. A. "60 Years of Databases (part four)." PROBLEMS IN PROGRAMMING, no. 2 (June 2022): 57–95. http://dx.doi.org/10.15407/pp2022.02.057.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emer- gence formation and rapid development, the era of relational databases, extended relational data- bases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of the era of relation-al databases, the results of E. Codd’s scientific activities, the theory of dependencies and normal forms, query languages, experimental research and development, optimization and standardiza- tion, and transaction management are revealed. The extended relational databases phase is devot- ed to describing temporal, spatial, deductive, ac- tive, object, distributed and statistical databases, array databases, and database machines and data warehouses. At the next stage, the problems of post-relational databases are disclosed, namely, NOSQL-, NewSQL- and ontological databases. The sixth stage is devoted to the disclosure of the causes of occurrence, characteristic properties, classification, principles of work, methods and technologies of big data. Finally, the last section provides a brief overview of database research and development in the former Soviet Union.

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Beech, David, and Çetin Özbütün. "Object databases as generalizations of relational databases." Computer Standards & Interfaces 13, no. 1-3 (October 1991): 221–30. http://dx.doi.org/10.1016/0920-5489(91)90030-4.

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Karunaratna, Damitha D. "BUILDING ONTOLOGIES OVER RELATIONAL DATABASES." International Journal of Research -GRANTHAALAYAH 6, no. 11 (November 30, 2018): 254–65. http://dx.doi.org/10.29121/granthaalayah.v6.i11.2018.1123.

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Relational Databases are typically created to fulfil the information requirements of a community of users generally belongs to a single organization. Data stored in these databases were typically accessed by using Structured Query Languages or through customized interfaces. With the popularity of the World Wide Web and the availability of large number of Relational Databases for public access there is a need for users to retrieve data from these databases by using a text-based queries, possibly by using the terms that they are familiar with. However, the inherent limitations of Structured Query Languages used to create and access data in relational Data Bases does not allow uses to access data by using text-based queries. Also, the terms used in queries should be limited to those used during the construction of the databases. This paper proposes an architecture to generated ontologies over relation databases and show how they could be enhanced semantically by using available domain-specific or top-level ontologies so that the data managed by the DBs can be accessed by using text-based queries. The feasibility of the proposed architecture was demonstrated by building a prototype system over a sample MySQL database.

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Damitha, D. Karunaratna. "BUILDING ONTOLOGIES OVER RELATIONAL DATABASES." International Journal of Research - Granthaalayah 6, no. 11 (November 30, 2018): 254–65. https://doi.org/10.5281/zenodo.1929732.

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Relational Databases are typically created to fulfil the information requirements of a community of users generally belongs to a single organization. Data stored in these databases were typically accessed by using Structured Query Languages or through customized interfaces. With the popularity of the World Wide Web and the availability of large number of Relational Databases for public access there is a need for users to retrieve data from these databases by using a textbased queries, possibly by using the terms that they are familiar with. However, the inherent limitations of Structured Query Languages used to create and access data in relational Data Bases does not allow uses to access data by using text-based queries. Also, the terms used in queries should be limited to those used during the construction of the databases. This paper proposes an architecture to generated ontologies over relation databases and show how they could be enhanced semantically by using available domain-specific or top-level ontologies so that the data managed by the DBs can be accessed by using text-based queries. The feasibility of the proposed architecture was demonstrated by building a prototype system over a sample MySQL database.

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Győrödi, Cornelia A., Tudor Turtureanu, Robert Ş. Győrödi, and Doina R. Zmaranda. "Implementing a Synchronization Method between a Relational and a Non-Relational Database." Big Data and Cognitive Computing 7, no. 3 (September 18, 2023): 153. http://dx.doi.org/10.3390/bdcc7030153.

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The accelerating pace of application development requires more frequent database switching, as technological advancements demand agile adaptation. The increase in the volume of data and at the same time, the number of transactions has determined that some applications migrate from one database to another, especially from a relational database to a non-relational (NoSQL) alternative. In this transition phase, the coexistence of both databases becomes necessary. In addition, certain users choose to keep both databases permanently updated to exploit the individual strengths of each database in order to streamline operations. Existing solutions mainly focus on replication, failing to adequately address the management of synchronization between a relational and a non-relational (NoSQL) database. This paper proposes a practical IT approach to this problem and tests the feasibility of the proposed solution by developing an application that maintains the synchronization between a MySQL database as a relational database and MongoDB as a non-relational database. The performance and capabilities of the solution are analyzed to ensure data consistency and correctness. In addition, problems that arose during the development of the application are highlighted and solutions are proposed to solve them.

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Minukhin, Serhii. "PERFORMANCE STUDY OF THE DTU MODEL FOR RELATIONAL DATABASES ON THE AZURE PLATFORM." Innovative Technologies and Scientific Solutions for Industries, no. 1 (19) (April 26, 2022): 27–39. http://dx.doi.org/10.30837/itssi.2022.19.027.

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When solving problems of working with relational databases on cloud platforms, the problem arises of choosing a specific model to ensure the performance of executing queries of varying complexity. The object of research is the processes of implementing various types of queries to relational databases within the framework of the DTU purchase model of the MS Azure platform. The subject is methods for evaluating the performance of work with relational databases based on the timing of query execution and indicators of the load on the resources of the cloud platform. The aim of the study is to develop a system of indicators for monitoring the current state of work with the database for reasonable decision-making on the choice of a certain price category of the DTU model of the MS Azure cloud service, which will optimize the results of working with the database. platforms Achieving the set goals involves the following tasks: to analyze modern tools and services for working with databases, in particular relational databases, on Azure and AWS cloud platforms, the features of their application and implementation; develop software for generating test relational databases of different sizes; test the generated databases on a local resource; taking into account the characteristics of the levels of the Azure DTU model, develop a new system of performance indicators, which includes 2 groups - time indicators and indicators of the load on existing platform resources; develop and implement queries of varying complexity for the generated test database for different levels of the DTU model and analyze the results. Methods. The following methods were used in the research: methods of relational database design; methods of creating queries in SQL-oriented databases with any number of tables; methods of creating and migrating data to cloud platforms; methods of monitoring the results of queries based on time and resource indicators; methods of generating test data for relational databases; system approach for complex assessment and analysis of productivity of work with relational databases. Results. On the basis of the developed scorecard used for the current analysis of the processes of working with relational databases of the MS Azure platform, numerous experiments were carried out for different levels of the model for simple and complex queries to a database with a total volume of 20 GB: loading of DTU model levels when executing various queries, the influence of model levels DTU Azure SQL database on the performance of simple and complex queries, the dependence of the execution time of various queries on the load of the CPU and the speed of write/read operations for different levels of the model. Conclusions. The results of the experiments allow us to conclude that the levels of the DTU model - S3 and S7 - are used to generate test data of various sizes (up to 20 GB) and execute database queries. The practical use of the proposed indicators to evaluate the results of applying the DTU model will improve the efficiency of decision-making on choosing the model level when implementing various queries and generating test data on the Azure cloud platform. The developed set of indicators for working with relational databases on the Azure cloud platform expands the basis of the methodological framework for evaluating the performance of working with relational databases on cloud platforms by analyzing the results of executing the simple and complex database queries on the resources involved.

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Thomer, Andrea K., and Karen M. Wickett. "Relational data paradigms: What do we learn by taking the materiality of databases seriously?" Big Data & Society 7, no. 1 (January 2020): 205395172093483. http://dx.doi.org/10.1177/2053951720934838.

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Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies (e.g., relational or graph-based database management systems), but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, we ask: How do the materialities of non-conventional databases differ from or align with the materialities of conventional relational systems? What properties of the database do the creators of these artifacts invoke in their rhetoric describing these systems—or in the data models underlying these digital objects? To answer these questions, we conducted a close analysis of four non-conventional scientific databases. By examining the materialities of information representation in each case, we show how scholarly communication regimes shape database materialities— and how information organization paradigms shape scholarly communication. These cases show abandonment of certain constraints of relational database construction alongside maintenance of some key relational data organization strategies. We discuss the implications that these relational data paradigms have for data use, preservation, and sharing, and discuss the need to support a plurality of data practices and paradigms.

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Pokorný, Jaroslav. "Integration of Relational and NoSQL Databases." Vietnam Journal of Computer Science 06, no. 04 (November 2019): 389–405. http://dx.doi.org/10.1142/s2196888819500210.

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The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into NoSQL and to perform data migration between the relational and NoSQL databases. Another possibility is to integrate a NoSQL database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated database architectures.

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Imam, Abdullahi Abubakar, Shuib Basri, Rohiza Ahmad, Amirudin A. Wahab, María T. González-Aparicio, Luiz Fernando Capretz, Ammar K. Alazzawi, and Abdullateef O. Balogun. "DSP: Schema Design for Non-Relational Applications." Symmetry 12, no. 11 (October 30, 2020): 1799. http://dx.doi.org/10.3390/sym12111799.

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The way a database schema is designed has a high impact on its performance in relational databases, which are symmetric in nature. While the problem of schema optimization is even more significant for NoSQL (“Not only SQL”) databases, existing modeling tools for relational databases are inadequate for this asymmetric setting. As a result, NoSQL modelers rely on rules of thumb to model schemas that require a high level of competence. Several studies have been conducted to address this problem; however, they are either proprietary, symmetrical, relationally dependent or post-design assessment tools. In this study, a Dynamic Schema Proposition (DSP) model for NoSQL databases is proposed to handle the asymmetric nature of today’s data. This model aims to facilitate database design and improve its performance in relation to data availability. To achieve this, data modeling styles were aggregated and classified. Existing cardinality notations were empirically extended using synthetically generated queries. A binary integer formulation was used to guide the mapping of asymmetric entities from the application’s conceptual data model to a database schema. An experiment was conducted to evaluate the impact of the DSP model on NoSQL schema production and its performance. A profound improvement was observed in read/write query performance and schema production complexities. In this regard, DSP has significant potential to produce schemas that are capable of handling big data efficiently.

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Yoan, Antonio Lopez Rodriguez, Hidalgo Delgado Yusniel, and Silega Martinez Nemury. "Linkage scenarios of relational databases and ontologies: a systematic mapping." Enfoque UTE 12, no. 4 (October 1, 2021): 58–75. https://doi.org/10.29019/enfoqueute.759.

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Relational databases are one of the most used data sources. However, as a storage source, they present a group of shortcomings. It is complex to store semantic knowledge in relational databases. To solve the deficiencies in knowledge representation of relational databases, one trend has been to use ontologies. Ontologies possess a richer semantic and are closer to the end user vocabulary than relational database schemas. The objective of the present research was to carry out a systematic mapping about the scenarios where relational databases and ontologies are linked to provide a better integration, query, and visualization of stored data. The mapping was carried out by applying a methodological proposal established in the literature. As outcomes of the research, it was detected that the mapping of relational databases to ontologies and the ontologies usage for the integration of heterogeneous data sources were the most common scenarios. Likewise, trends and challenges were identified in each scenario, which might deserve further research efforts in the future.

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Princz, Mária. "Trends and Challenges of Databases." International Journal of Engineering and Management Sciences 3, no. 5 (December 10, 2018): 71–75. http://dx.doi.org/10.21791/ijems.2018.5.8.

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The database management, using relational databases, is part of curriculum in the Hungarian high schools. The aim of this paper is to present how we can show for students the challenges facing data processing, data retrieval, beyond the relational database management taught in high school.

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Blessing, E. James. "HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENT." International Journal of Computer Science, Engineering and Applications (IJCSEA) 7, no. 3/4 (February 19, 2020): 15–27. https://doi.org/10.5281/zenodo.3674667.

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Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.

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Ait El Mouden, Zakariyaa, and Abdeslam Jakimi. "A New Algorithm for Storing and Migrating Data Modelled by Graphs." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 11 (October 5, 2020): 137. http://dx.doi.org/10.3991/ijoe.v16i11.15545.

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NoSQL databases have moved from theoretical solutions to exceed relational databases limits to a practical and indisputable application for storing and manipulation big data. In term of variety, NoSQL databases store heterogeneous data without being obliged to respect a predefined schema such as the case of relational and object-relational databases. NoSQL solutions surpass the traditional databases in storage capacity; we consider MongoDB for example, which is a document-oriented database capable of storing unlimited number of documents with a maximal size of 32TB depending on the machine that runs the database and also the operating system. Also, in term of velocity, many researches compared the execution time of different transactions and proved that NoSQL databases are the perfect solution for real-time applications. This paper presents an algorithm to store data modeled by graphs as NoSQL documents, the purpose of this study is to exploit the high amount of data stored in SQL databases and to make such data usable by recent clustering algorithms and other data science tools. This study links relational data to document datastores by defining an effective algorithm for reading relational data, modelling those data as graphs and storing those data as NoSQL documents.

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