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Journal articles on the topic 'Relational database system'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

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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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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Albert, Arnauld, and Cristiano Bozza. "The relational database system of KM3NeT." EPJ Web of Conferences 116 (2016): 07004. http://dx.doi.org/10.1051/epjconf/201611607004.

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Glass, Alan J., and Neal M. Holtz. "A relational database for building structures." Canadian Journal of Civil Engineering 13, no. 5 (October 1, 1986): 528–34. http://dx.doi.org/10.1139/l86-080.

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This paper looks at some of the requirements of a database for building structure data and the role of the building structure database in an integrated design system. A method of representing the necessary building structure data in a relational database is discussed. The basic data manipulation commands for relational databases are briefly explained. A demonstration database for a steel structure implemented on a microcomputer using a commercial relational database is presented. Key words: computer-aided design (CAD), database, database management system (DBMS), relational database, engineering database, building description.

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Tho, Do Xuan, and Dinh Thi Ngoc Thanh. "TN-82 — a relational database management system." Journal of Computer Science and Cybernetics 1, no. 3 (August 6, 2015): 13–21. http://dx.doi.org/10.15625/1813-9663/1/3/6689.

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Using the relational approach of database systems, the TN-82 has been developed and implemented on a minicomputer. The system consists of four subsystems which have relatively independent functions. These subsystems can be modified and extended, if it is necessary to obtain a new one, which adapts another practical problem with few costs. The system TN-82 is a structured system and controlled by hierarchical principles. The method based on module programming has been used. Data independence, data consistency, some problems of data security and integrity are guaranteed. Many blocks of information stored in the system can be unified. The system has been constructed in such a way that the redundant information is eliminated as much as possible. With a simple but powerful Vietnamese query manguage, a user is able to easily formulate his non-procedural queries. The query processing is based on implementing in the system of almost operations of relational algebra (such as selection, projection, join, union, difference, intersection). Some problems of query optimizing are resolved and applied in this system, such as analyzing and choosing the orders of executed operations, selecting access paths,… The system TN-82 can be easily realized on the other more powerful computers.

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Matallah, Houcine, Ghalem Belalem, and Karim Bouamrane. "Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database." International Journal of Software Science and Computational Intelligence 13, no. 3 (July 2021): 38–63. http://dx.doi.org/10.4018/ijssci.2021070104.

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NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.

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7

Ahmad, Khaleel, Mohammad Shoaib Alam, and Nur Izura Udzir. "Security of NoSQL Database Against Intruders." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 5–12. http://dx.doi.org/10.2174/1872212112666180731114714.

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Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.

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8

Berezky, O. M., O. Yo Pitsun, S. O. Verbovyi, and T. V. Datsko. "RELATIONAL DATABASE OF INTELLIGENT AUTOMATED MICROSCOPY SYSTEM." Scientific Bulletin of UNFU 27, no. 5 (August 29, 2017): 125–29. http://dx.doi.org/10.15421/40270525.

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Scotti, Alex, Mark Hannum, Michael Ponomarenko, Dorin Hogea, Akshat Sikarwar, Mohit Khullar, Adi Zaimi, et al. "Comdb2 bloomberg's highly available relational database system." Proceedings of the VLDB Endowment 9, no. 13 (September 2016): 1377–88. http://dx.doi.org/10.14778/3007263.3007275.

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10

Logan, Paula M., Clifford W. Hale, and John M. Atkins. "An academic relational algebra database management system." ACM SIGCSE Bulletin 22, no. 3 (August 1990): 25–28. http://dx.doi.org/10.1145/101085.101093.

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Malecha, Gregory, Greg Morrisett, Avraham Shinnar, and Ryan Wisnesky. "Toward a verified relational database management system." ACM SIGPLAN Notices 45, no. 1 (January 2, 2010): 237–48. http://dx.doi.org/10.1145/1707801.1706329.

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Patnaik, L. M., and P. S. Gill. "GRDB: A general purpose relational database system." Information Systems 10, no. 2 (January 1985): 169–80. http://dx.doi.org/10.1016/0306-4379(85)90034-1.

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Yong-qiang, Sun, Xu Shu-ting, Zhu Feng-hua, and Lai Shu-hua. "PORLES: A parallel object-relational database system." Wuhan University Journal of Natural Sciences 6, no. 1-2 (March 2001): 100–109. http://dx.doi.org/10.1007/bf03160230.

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Budd, Jeffrey R., Warren J. Warwick, Catherine L. Wielinski, and Stanley M. Finkelstein. "A medical information relational database system (MIRDS)." Computers and Biomedical Research 21, no. 5 (October 1988): 419–33. http://dx.doi.org/10.1016/0010-4809(88)90002-x.

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Lee, Sangjae, and Ingoo Han. "EDI controls design support system using relational database system." Decision Support Systems 29, no. 2 (August 2000): 169–93. http://dx.doi.org/10.1016/s0167-9236(00)00071-3.

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Ristic, Sonja, Slavica Aleksic, Milan Celikovic, and Ivan Lukovic. "Generic and standard database constraint meta-models." Computer Science and Information Systems 11, no. 2 (2014): 679–96. http://dx.doi.org/10.2298/csis140216037r.

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Many software engineering activities entail dealing with legacy information systems. When these systems become too costly to maintain, or when new technologies need to be incorporated, they need to be replaced or somehow reengineered. This can be done with significantly reduced amount of effort and cost if the conceptual models of these systems are available. Reverse engineering is the process of analyzing a subject system to create representations of the system at a higher level of abstraction. Relational databases are a common source of reverse engineering. Starting from a physical database schema, that is recorded into relational database schema data repository, the conceptual database schema or logical database schema could be extracted. The extraction process may be seen as a chain of model-to-model transformations that trace model elements from a model at the lower level of abstraction to a model at the higher level of abstraction, achieved through meta-modeling. In the paper we present generic and standard database constraint meta-models, focusing on multi-relational database constraints captured in a legacy database. These meta-models are aimed at support of model transformations to create conceptual models, as a useful source for the system reengineering process.

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Pangalos, G. "Development of an experimental hospital database system using a relational database management system." Medical Informatics 14, no. 4 (January 1989): 309–21. http://dx.doi.org/10.3109/14639238908999285.

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Тереник, Дмитро, and Георгій Кучук Анатолійович. "ПОРІВНЯННЯ SQL І NOSQL БАЗ ДАНИХ НА ПРИКЛАДІ ПРОЕКТУВАННЯ АФФІЛЕЙТ РЕПОРТ СИСТЕМ." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (January 28, 2020): 83–89. http://dx.doi.org/10.32620/reks.2020.1.08.

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Nowadays, due to the rapid development of social networks and the blogger culture, there is a tendency to use affiliate systems to promote their product. The Affiliate Reporting Service is a service offered to customers who want to analyze the affiliate systems' performance data. These systems are used by business executives and business owners to analyze ecommerce data and convert it into profit/expense data to adjust their business path further. This type of service includes data storage for all affiliates, data archive management, conversion of advertising campaigns, trend tracking, and more. These systems are based on large data sets that need to be stored correctly and safely stored and processed using database management systems. There are two major direction: SQL and NoSQL, relational and non-relational databases. The differences between them are how they are designed, what types of data they support, how they store information, how they support information security. A rigid relational database schema helps maintain the security and integrity of data when stored and modified. The lack of a rigid database schema and the need to change the entire structure of the table with a minimal change in the storage concept, make it easier to work with non-relational databases and subsequently support them, but it also has its disadvantages. It is important to understand that the tasks are different and the methods for solving them are also different; Choosing a database and database management system is a complex multi-parameter task and is one of the most important steps in developing such applications. Properly selected database will reduce the monetary and time costs associated with the development of the software, as well as facilitate system support in the future. The purpose of the article is to compare relational and non-relational databases by different metrics used in Affiliate Reporting Systems Design. In particular, a performance analysis was conducted on the performance of various operations, on the basis of which conclusions were drawn about the use of a particular database.

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19

Mahmood, Alza A. "Automated Algorithm for Data Migration from Relational to NoSQL Databases." Al-Nahrain Journal for Engineering Sciences 21, no. 1 (February 10, 2018): 60. http://dx.doi.org/10.29194/njes21010060.

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One of the barriers that the developer community face once turning to the newly, highly distributable, schema agnostic and non-relational database, called NoSQL, which is how to migrate their legacy relational database (which is already filled with a large amount of data) into this new class of database management systems. This paper presents a new approach for converting the already filled relational database of any database management system to any type of NoSQL databases in the most optimized data structure form without bothering of specifying the schema of tables and relations between them. In addition, a simplified software as a prototype based on this algorithm is built to show the results of the output for testing the validity of the algorithm.

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20

Shekhel, Alex, and Mike O'Brien. "Selecting a Relational Database Management System for Library Automation Systems." Library Hi Tech 7, no. 2 (February 1989): 17–20. http://dx.doi.org/10.1108/eb047754.

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21

Almeida, Fernando, Pedro Silva, and Fernando Araújo. "Performance Analysis and Optimization Techniques for Oracle Relational Databases." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 117–32. http://dx.doi.org/10.2478/cait-2019-0019.

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Abstract Databases provide an efficient way to store, retrieve and analyze data. Oracle relational database is one of the most popular database management systems that is widely used in a different variety of industries and businesses. Therefore, it is important to guarantee that the database access and data manipulation is optimized for reducing database system response time. This paper intends to analyze the performance and the main optimization techniques (Forall, Returning, and Bulk Collect) that can be adopted for Oracle Relational Databases. The results have shown that the adoption of Forall and Bulk Collect approaches bring significant benefits in terms of execution time. Furthermore, the growth rate of the average execution time is lower for Bulk Collect than Forall. However, adoption of Returning approach doesn’t bring significant statistical benefits.

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Luo, Shangyu, Zekai J. Gao, Michael Gubanov, Luis L. Perez, and Christopher Jermaine. "Scalable Linear Algebra on a Relational Database System." IEEE Transactions on Knowledge and Data Engineering 31, no. 7 (July 1, 2019): 1224–38. http://dx.doi.org/10.1109/tkde.2018.2827988.

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23

Ruiu, D., R. Divi, P. Katzberg, and J. Katzberg. "Expert system and relational database improve planning productivity." IEEE Computer Applications in Power 5, no. 3 (July 1992): 39–44. http://dx.doi.org/10.1109/67.143273.

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Yang, Younghyoo. "Semantic-based Keyword Search System over Relational Database." Journal of the Korea Society of Computer and Information 18, no. 12 (December 31, 2013): 91–101. http://dx.doi.org/10.9708/jksci.2013.18.12.091.

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D'Andrea, Albert, and Phil Janus. "UniSQL's next-generation object-relational database management system." ACM SIGMOD Record 25, no. 3 (September 1996): 70–76. http://dx.doi.org/10.1145/234889.234902.

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Luo, Shangyu, Zekai J. Gao, Michael Gubanov, Luis L. Perez, and Christopher Jermaine. "Scalable Linear Algebra on a Relational Database System." ACM SIGMOD Record 47, no. 1 (September 10, 2018): 24–31. http://dx.doi.org/10.1145/3277006.3277013.

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Luo, Shangyu, Zekai J. Gao, Michael Gubanov, Luis L. Perez, Dimitrije Jankov, and Christopher Jermaine. "Scalable linear algebra on a relational database system." Communications of the ACM 63, no. 8 (July 22, 2020): 93–101. http://dx.doi.org/10.1145/3405470.

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28

Kozub, J. G., D. R. Lynch, G. C. Kozub, L. M. Kawchuk, and D. K. Fujimoto. "A relational database system for potato breeding programs." American Journal of Potato Research 77, no. 2 (March 2000): 95–101. http://dx.doi.org/10.1007/bf02853736.

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29

You, Kai He, Hong Yan Li, and Sheng Zhao. "Database Design for Archives Management System." Applied Mechanics and Materials 385-386 (August 2013): 1734–37. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1734.

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Archive management system based on modern information technology as the support, the archives information as management object, the archives work as the core and implement management information system, database design is the core work of the development of archives management system work, this article launches the research from four aspects according to the database design process. First, from the business requirements, functional requirements, performance requirements, such as security requirements for demand analysis; Second, according to the result of requirement analysis, conceptual design using E-R diagram; Third, converting E-R diagram logic structure supported by the database management system; Fourth, for the logic model to determine the storage structure and access method. In this paper, it based on the relational database design, a relational database with full-text database is the development direction of the future.

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Adji, Teguh Bharata, Dwi Retno Puspita Sari, and Noor Akhmad Setiawan. "Relational into Non-Relational Database Migration with Multiple-Nested Schema Methods on Academic Data." IJITEE (International Journal of Information Technology and Electrical Engineering) 3, no. 1 (September 13, 2019): 16. http://dx.doi.org/10.22146/ijitee.46503.

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The rapid development of internet technology has increased the need of data storage and processing technology application. One application is to manage academic data records at educational institutions. Along with massive growth of information, decrement in the traditional database performance is inevitable. Hence, there are many companies choose to migrate to NoSQL, a technology that is able to overcome the traditional database shortcomings. However, the existing SQL to NoSQL migration tools have not been able to represent SQL data relations in NoSQL without limiting query performance. In this paper, a relational database transformation system transforming MySQL into non-relational database MongoDB was developed, using the Multiple Nested Schema method for academic databases. The development began with a transformation scheme design. The transformation scheme was then implemented in the migration process, using PDI/Kettle. The testing was carried out on three aspects, namely query response time, data integrity, and storage requirements. The test results showed that the developed system successfully represented the relationship of SQL data in NoSQL, provided complex query performance 13.32 times faster in the migration database, basic query performance involving SQL transaction tables 28.6 times faster on migration results, and basic performance Queries without involving SQL transaction tables were 3.91 times faster in the migration source. This shows that the theory of the Multiple Nested Schema method, aiming to overcome the poor performance of queries involving many JOIN operations, is proved. In addition, the system is also proven to be able to maintain data integrity in all tested queries. The space performance test results indicated that the migrated database transformed using the Multiple Nested Schema method showed a storage requirement of 10.53 times larger than the migration source database. This is due to the large amount of data redundancy resulting from the transformation process. However, at present, storage performance is not a top priority in data processing technology, so large storage requirements are a consequence of obtaining efficient query performance, which is still considered as the first priority in data processing technology.

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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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Dorożyński, Janusz, and Maciej Przepiórka. "Estimating the performance efficiency of ERP system with the relational database in a heterogenic environment." Bulletin of the Military University of Technology 68, no. 1 (March 29, 2019): 33–49. http://dx.doi.org/10.5604/01.3001.0013.1465.

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The article presents a study of the performance of a commercial ERP system, operating in the 2W model with a relational database installed on the IBM mainframe platform, as well as business logic and presentation server installed on the Windows Server platform. The study was carried out in accordance with the developed methodology by means of recording the time of performing system functions indicated in the own document “Time-use profile of the ERP system under test”. Consulting sessions were also held. Based on the results of the research, a recommendation was made to migrate production relational databases to the Windows Server environment. Keywords: IT, relational database, ERP, MRPII, three-layer architecture, application performance.

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Huang, Yi, Xin Qiang Ma, Dan Ning Li, and Rong Wu. "Research and Applications of Access Control Based on Logic SQL Database System." Advanced Materials Research 143-144 (October 2010): 672–76. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.672.

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Access control is the process of mediating every request to data and services maintained by a system and determining whether the request should be granted or denied. Access control policies are security policies that govern access to resources. It can be applied to several new types of databases. LogicSQL is an object relational database management system implemented with the advanced (formula-lock based) concurrency control protocol, and the steged database architecture. In this paper, we discuss access control in multilevel database management systems applies and illustrate the main applies of access control in the LogicSQL database system.

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Mahnič, Viljan. "Using grid files for a relational database management system." Microprocessing and Microprogramming 31, no. 1-5 (April 1991): 13–18. http://dx.doi.org/10.1016/s0165-6074(08)80036-1.

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Mumick, Inderpal Singh, and Hamid Pirahesh. "Implementation of magic-sets in a relational database system." ACM SIGMOD Record 23, no. 2 (June 1994): 103–14. http://dx.doi.org/10.1145/191843.191860.

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Kim, Won. "UniSQL/X unified relational and object-oriented database system." ACM SIGMOD Record 23, no. 2 (June 1994): 481. http://dx.doi.org/10.1145/191843.191938.

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Harvey, Tina M., Craig W. Schnepf, and Mark A. Roth. "The design of the Triton nested relational database system." ACM SIGMOD Record 20, no. 3 (September 1991): 62–72. http://dx.doi.org/10.1145/126482.126490.

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Sacks-Davis, R., A. Kent, K. Ramamohanarao, J. Thom, and J. Zobel. "Atlas: a nested relational database system for text applications." IEEE Transactions on Knowledge and Data Engineering 7, no. 3 (June 1995): 454–70. http://dx.doi.org/10.1109/69.390250.

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Hagadone, Thomas R., and Michael S. Lajiness. "Capturing chemical information in an extended relational database system." Tetrahedron Computer Methodology 1, no. 3 (1988): 219–30. http://dx.doi.org/10.1016/0898-5529(88)90027-9.

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Powell, John, Gautami Erukulla, Mohamad Buhisi, and Balendran Velauthapillai. "A relational database management system for atmospheric compensation research." Computers & Geosciences 37, no. 4 (April 2011): 588–97. http://dx.doi.org/10.1016/j.cageo.2010.04.015.

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Zongyao, Sha, and Bian Fuling. "Spatio-temporal data model based on relational database system." Geo-spatial Information Science 5, no. 2 (January 2002): 22–27. http://dx.doi.org/10.1007/bf02833882.

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DOU, DEJING, HAN QIN, and PAEA LEPENDU. "ONTOGRATE: TOWARDS AUTOMATIC INTEGRATION FOR RELATIONAL DATABASES AND THE SEMANTIC WEB THROUGH AN ONTOLOGY-BASED FRAMEWORK." International Journal of Semantic Computing 04, no. 01 (March 2010): 123–51. http://dx.doi.org/10.1142/s1793351x10000961.

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Integrating existing relational databases with ontology-based systems is among the important research problems for the Semantic Web. We have designed a comprehensive framework called OntoGrate which combines a highly automatic mapping system, a logic inference engine, and several syntax wrappers that inter-operate with consistent semantics to answer ontology-based queries using the data from heterogeneous databases. There are several major contributions of our OntoGrate research: (i) we designed an ontology-based framework that provides a unified semantics for mapping discovery and query translation by transforming database schemas to Semantic Web ontologies; (ii) we developed a highly automatic ontology mapping system which leverages object reconciliation and multi-relational data mining techniques; (iii) we developed an inference-based query translation algorithm and several syntax wrappers which can translate queries and answers between relational databases and the Semantic Web. The testing results of our implemented OntoGrate system in different domains show that the large amount of data in relational databases can be directly utilized for answering Semantic Web queries rather than first converting all relational data into RDF or OWL.

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M. Korneev, A., A. K. Pogodaev, L. S. Abdullakh, and M. V. Salfetnikov. "Optimization structure database of complex systems." International Journal of Engineering & Technology 7, no. 2.13 (April 15, 2018): 133. http://dx.doi.org/10.14419/ijet.v7i2.13.11626.

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In the article such ways of optimization of the structure of a database of complex structured objects that allow simplifying the scheme of an information system. As a result, the response time of the information system to the user's request is reduced, and it becomes possible to more effectively manage and monitor the progress of ongoing processes. A formalized model of the relational expression is developed, a model of the corresponding equivalent transformation of the chosen relational expression is created, a formal description of the developed algorithm for optimizing the queries is presented.

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BLANCO, JOSÉ MIGUEL, ARANTZA ILLARRAMENDI, and ALFREDO GOÑI. "BUILDING A FEDERATED RELATIONAL DATABASE SYSTEM: AN APPROACH USING A KNOWLEDGE-BASED SYSTEM." International Journal of Cooperative Information Systems 03, no. 04 (December 1994): 415–55. http://dx.doi.org/10.1142/s0218215794000211.

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Due to the emerging interest in integrating different application environments, there have been many recent proposals for federated systems. In this paper, a federated system that permits the integration of heterogeneous relational databases using a terminological knowledge representation system is presented. In particular, two of the system's components: the translator and the integrator are explained in depth. The translator permits one to obtain a terminology from a relational schema, either semiautomatically, by expressing database properties, or manually, by using a set of predefined operations. In turn, the integrator generates a federated terminology by integrating several terminologies using the semantics expressed as correspondences between the data elements of different terminologies. Unlike many other approaches, the use of a terminological system permits us to obtain a semantically richer federated terminology and, at the same time, define a wider and more consistent integration process.

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Huda, M. Misbachul, Dian Rahma Latifa Hayun, and Zhin Martun. "Data Modeling for Big Data." Jurnal ULTIMA InfoSys 6, no. 1 (December 1, 2015): 1–11. http://dx.doi.org/10.31937/si.v6i1.273.

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Today the rapid growth of the internet and the massive usage of the data have led to the increasing CPU requirement, velocity for recalling data, a schema for more complex data structure management, the reliability and the integrity of the available data. This kind of data is called as Large-scale Data or Big Data. Big Data demands high volume, high velocity, high veracity and high variety. Big Data has to deal with two key issues, the growing size of the datasets and the increasing of data complexity. To overcome these issues, today researches are devoted to kind of database management system that can be optimally used for big data management. There are two kinds of database management system, relational database management system and nonrelational system that can be optimally used for big data management. There are two kinds of database management, Relational Database Management and Non-relational Database Management. This paper will give reviews about these two database management system, including description, vantage, structure and the application of each DBMS. Index Terms - Big Data, DBMS, Large-scale Data, Non-relational Database, Relational Database.

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Alshammari, Bandar M. "Security Assessment Model for Database Relational Designs." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1171–80. http://dx.doi.org/10.1166/jmihi.2019.2715.

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The increasing number of data breaches has led many organizations to focus on securing their IT infrastructures and application architectures. However, the main causes of many of the latest attacks are not associated with these two architectures. The damage caused by most of the recent attacks could have been minimized if more attention was given to enhancing the security of all components of the database architecture. The existing enterprise database architecture frameworks do not consider this issue a priority; hence, it has received minimal attention. The enterprise database architecture is the most important architecture because it is responsible for defining how all types of data, whether security-critical or not, are stored and accessed. This paper focuses on addressing the lack of a complete solution to help enterprise system architects to address the security of their organizations from early stages. The novelty of this approach is that it specifies how to modify the required artifacts by the enterprise database architecture to address security-critical data. The approach also defines a number of security measurements that help enterprise architects in measuring the security of the organization database based on those artifacts. These metrics are developed based on the results of a cybersecurity experiment conducted on 100 randomly selected open-source websites. The paper's contributions also consist of the definition of a number of security refactoring rules that specify how to modify current enterprise databases to make them more secure. This paper uses an existing relational diagram for a health clinic database to illustrate the application of the model to an existing database. The validity and applicability of these metrics and refactoring rules are proved using an experiment conducted on a number of security-related databases.

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Husain, Al, Felita Ariyanti, and Sinudarwati Sinudarwati. "PERANCANGAN DATABASE RELATIONAL PADA TOKO BUKU ONLINE." Journal CERITA 2, no. 2 (August 1, 2016): 133–41. http://dx.doi.org/10.33050/cerita.v2i2.677.

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Journal report titled "Designing Database at Online Bookstore" Task Journal report aims to design a database to facilitate the process of buying and selling online for consumers. Besides the new system design is also done with the preparation of the overall system that consists of data normalization, ERD, database file. The methodology of the research is includes field studies and literature. Field studies include observations, interviews. Literature study is being conducted by the research literature relevant to the problem. Once the global design is finished it can be seen that report generation earlier with manual processes by collecting data one by one so that the time allowed is not enough, then the procurement database design report will be easier.

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Gorskis, Henrihs, and Arkady Borisov. "Storing an OWL 2 Ontology in a Relational Database Structure." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 71. http://dx.doi.org/10.17770/etr2015vol3.168.

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<p class="R-AbstractKeywords"><span lang="EN-GB">This paper examines the possibility of storing OWL 2 based ontology information in a classical relational database and reviews some existing methods for ontology databases. In most cases a database is a fitting solution for storing and sharing information among systems, clients or agents. Similarly, in order to make domain ontology information more accessible to systems, in a comparable way, it can be stored and provided in a database form. As of today, there is no consensus on a specific ontology database structure. The main focus of this paper is specifically on OWL 2 as a basis for the description of ontology centric information in a database. The Web Ontology Language OWL 2 is a language for describing ontology information for the Semantic Web. As such it consists of a list of reserved words and grammatical rules for defining many parts of ontology knowledge. Based on this language specification this paper examines the possibility of storing information in a relational database for the description of domain ontology information. By creating a database structure based on OWL2 it is feasible to obtain an approach to storing information about the domain ontology in an utilizable way, by using its descriptive abilities. Nowadays multiple approaches to storing ontology information and OWL in databases exist; most of them are based on storing RDF data or provide persistence for specific OWL software libraries. The examination of the existing approaches provided in this paper, shows how they differ from the goal of obtaining a general, more easily usable and less software library specific database for domain ontology centric information. This paper describes a version of a simple relational database capable of holding and providing ontology knowledge on demand, which can be implemented on a database management system of choice. </span></p>

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Pokorný, Jaroslav. "Integration of Relational and Graph Databases Functionally." Foundations of Computing and Decision Sciences 44, no. 4 (December 1, 2019): 427–41. http://dx.doi.org/10.2478/fcds-2019-0021.

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Abstract In today’s multi-model database world there is an effort to integrate databases expressed in different data models. The aim of the article is to show possibilities of integration of relational and graph databases with the help of a functional data model and its formal language – a typed lambda calculus. We suppose the existence of a data schema both for the relational and graph database. In this approach, relations are considered as characteristic functions and property graphs as sets of single-valued and multivalued functions. Then it is possible to express a query over such integrated heterogeneous database by one query expression expressed in a version of the typed lambda calculus. A more user-friendly version of such language could serve as a powerful query tool in practice. We discuss also queries sent to the integrated system and translated into queries in SQL and Cypher - the graph query language for Neo4j.

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Abdulrahman Kareem Zada, Ashna. "Assess the Changing Role of Database Technology within Geographic Information System Over the Past 45 Years." Polytechnic Journal 9, no. 1 (June 30, 2019): 26–31. http://dx.doi.org/10.25156/ptj.v9n1y2019.pp26-31.

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Spatial data stored in databases have been become progressively crucial in the geographic information system (GIS) field within recent years. Spatial data are increasingly being noted for its significance in representing for real-life relationships. The value of spatial databases cannot be underestimated since it has been established to be an efficient and flexible means of handling enormous spatial datasets. This paper shows the different methods, in which spatial data are integrated by major database vendors and database within GIS. The current report has also examined the new technologies and advancements by database and GIS vendors in relational database management systems in the past four decades, which have facilitated spatial data to become integrated into databases.

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