Статті в журналах: "Relational databases"

1

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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2

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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3

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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4

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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5

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.

6

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.

7

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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8

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.

9

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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10

., 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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11

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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12

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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13

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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14

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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15

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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16

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. 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.

17

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.

18

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

19

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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20

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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21

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.

22

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.

23

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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24

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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25

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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26

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.

27

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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28

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.

29

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.

30

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.

31

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.

32

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.

34

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.

35

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.

36

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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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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Chiang, Roger H. L., Terence M. Barron, and Veda C. Storey. "Reverse engineering of relational databases: Extraction of an EER model from a relational database." Data & Knowledge Engineering 12, no. 2 (March 1994): 107–42. http://dx.doi.org/10.1016/0169-023x(94)90011-6.

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Deepa, S. "A Query Optimization Framework for Fuzzy Relational Databases." Asian Journal of Engineering and Applied Technology 1, no. 1 (May 5, 2012): 43–46. http://dx.doi.org/10.51983/ajeat-2012.1.1.2502.

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Ever since the development of relational model, relational database systems have been extensively studied and several commercial relational database systems are currently available. Relational model usually take care of only well defined data. In order to capture more meaning to the data an extension of the classical relational model called fuzzy relational model was proposed. The key reasons for the success of relational database lies in the power of declarative languages and execution strategies used in query optimization. Estimating the cost of fuzzy query based on system catalog introduces error due to approximation involved and insufficient information at query execution time. So there is need for a query optimization framework that addresses the issues of query execution in fuzzy relational databases. This paper deals with a framework for building fuzzy cost model to obtain a good execution strategy for a query.

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Arif, Dashne Raouf, and Nzar Abdulqadir Ali. "Improving the performance of big data databases." Kurdistan Journal of Applied Research 4, no. 2 (December 31, 2019): 206–20. http://dx.doi.org/10.24017/science.2019.2.20.

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Real-time monitoring systems utilize two types of database, they are relational databases such as MySQL and non-relational databases such as MongoDB. A relational database management system (RDBMS) stores data in a structured format using rows and columns. It is relational because the values of the tables are connected. A non-relational database is a database that does not adopt the relational structure given by traditional. In recent years, this class of databases has also been referred to as Not only SQL (NoSQL). This paper discusses many comparisons that have been conducted on the execution time performance of types of databases (SQL and NoSQL). In SQL (Structured Query Language) databases different algorithms are used for inserting and updating data, such as indexing, bulk insert and multiple updating. However, in NoSQL different algorithms are used for inserting and updating operations such as default-indexing, batch insert, multiple updating and pipeline aggregation. As a result, firstly compared with related papers, this paper shows that the performance of both SQL and NoSQL can be improved. Secondly, performance can be dramatically improved for inserting and updating operations in the NoSQL database compared to the SQL database. To demonstrate the performance of the different algorithms for entering and updating data in SQL and NoSQL, this paper focuses on a different number of data sets and different performance results. The SQL part of the paper is conducted on 50,000 records to 3,000,000 records, while the NoSQL part of the paper is conducted on 50,000 to 16,000,000 documents (2GB) for NoSQL. In SQL, three million records are inserted within 606.53 seconds, while in NoSQL this number of documents is inserted within 67.87 seconds. For updating data, in SQL 300,000 records are updated within 271.17 seconds, while for NoSQL this number of documents is updated within just 46.02 seconds.

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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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<span>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.</span>

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Abdullah, Ahmad, and Qingfeng Zhuge. "From Relational Databases to NoSQL Databases: Performance Evaluation." Research Journal of Applied Sciences, Engineering and Technology 11, no. 4 (October 5, 2015): 434–39. http://dx.doi.org/10.19026/rjaset.11.1799.

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Bouamama, Samah. "Migration from a Relational Database to NoSQL." International Journal of Knowledge-Based Organizations 8, no. 3 (July 2018): 63–80. http://dx.doi.org/10.4018/ijkbo.2018070104.

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This article describes how due to the monstrous evolution of the technology and the enormous increase in data, it becomes difficult to work with traditional database management tools; relational databases quickly reach their limits and adding servers does not increase performance. As a result of this problem, new technologies have emerged, such as NoSQL databases, which radically change the architecture of the databases that the authors are used to seeing, thus increasing the performance and availability of services. As these technologies are relatively new, standard or formal migration processes do not yet exist, the authors thought it useful to propose a migration approach from a relational database to a database-oriented columns type HBase and Cassandra.

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Petkov, Yulian Ivanov, and Alexandre Ivanov Chikalanov. "Innovative Proposals for Database Storage and Management." Mathematics and Informatics LXV, no. 1 (February 28, 2022): 45–52. http://dx.doi.org/10.53656/math2022-1-6-inn.

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At present, the problem of storing large data sets as a source of artificial intelligence acquires a geopolitical and strategic character. The most well-known and used type of databases so far are the relational (SQL databases) and nonrelational (NoSQL databases. The both approaches have some principle problems, which are described below. That publication presents two original approaches to overcoming some of these shortcomings. First one is Object-oriented model for storing data in a relational database. The second is Storage of non-relational data in a relational database according to previously freely created by the user models. Presented models were used as base for software development of more than ten middle and large size national and European scientific and industrial projects.

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Griffin, Chris J. "Relational databases for medical audit." Medical Journal of Australia 166, no. 12 (June 1997): 672. http://dx.doi.org/10.5694/j.1326-5377.1997.tb123321.x.

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S. Zhou, and G. Meng. "Semantic Query for Relational Databases." International Journal of Digital Content Technology and its Applications 5, no. 10 (October 31, 2011): 166–72. http://dx.doi.org/10.4156/jdcta.vol5.issue10.20.

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Tinelli, Eufemia, Francesco M. Donini, and Eugenio Di Sciascio. "Compiling subsumption to relational databases." Intelligenza Artificiale 7, no. 1 (2013): 19–29. http://dx.doi.org/10.3233/ia-130044.

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Bhat, Uma, and Shraddha Jadhav. "Moving Towards Non-Relational Databases." International Journal of Computer Applications 1, no. 13 (February 25, 2010): 40–46. http://dx.doi.org/10.5120/284-446.

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Pistol, Luminita, and Radu BUCEA-MANEA-TONIS. "Logical Querying of Relational Databases." Journal of Economic Development, Environment and People 5, no. 4 (December 30, 2016): 58. http://dx.doi.org/10.26458/jedep.v5i4.518.

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This paper aims to demonstrate the usefulness of formal logic and lambda calculus in database programming. After a short introduction in propositional and first order logic, we implement dynamically a small database and translate some SQL queries in filtered java 8 streams, enhanced with Tuples facilities from jOOλ library.

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