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Journal articles on the topic 'Relational DBMS'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Győrödi, Cornelia A., Diana V. Dumşe-Burescu, Doina R. Zmaranda, Robert Ş. Győrödi, Gianina A. Gabor, and George D. Pecherle. "Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage." Applied Sciences 10, no. 23 (November 28, 2020): 8524. http://dx.doi.org/10.3390/app10238524.

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In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

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2

Mohan, C. "IBM's relational DBMS products." ACM SIGMOD Record 22, no. 2 (June 1993): 445–48. http://dx.doi.org/10.1145/170036.170129.

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3

FONG, JOSEPH, and SHI-MING HUANG. "ARCHITECTURE OF A UNIVERSAL DATABASE: A FRAME MODEL APPROACH." International Journal of Cooperative Information Systems 08, no. 01 (March 1999): 47–82. http://dx.doi.org/10.1142/s0218843099000046.

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RDB has been dominant in the industry for the last decade. OODB is recongnized as a post-relational technology that can improve productivity, Hierarchical Database and Network Database were popular in the 1970s, and have been developed into legacy database systems. The DBMS of various data models have proliferated into many companies, and become their important assets. There is a need to integrate these database system into a data warehouse in the company. We investigate a solution to the problem by offering an architecture of a universal database for the connectivity of various DBMSs using different data models. A frame model is chosen to represent the conceptual and logical schema of the universal database, which structures an application domain into classes organized via generalization, aggregaton and user-defined relationships, and its data in relational tables. The schemas of the existing database systems are translated into frame model conceptual shcemas which are integrated into a global frame model in a knowledge representation that includes classes for object structure descriptions and constraints for supporting user-defined relationships. The universal database is implemented by a relational DBMS as a kernel. In addition to relational tables, the universal database consists of program area to emulate database navigation in nonrelatoinal DBMS, method classes to implement program calls to emulate methods of OODBMS, and contraint classes to preserve semantic and resolve naming conflicts in schema translation and integration. To ensure each database program access the universal database, a database gateway for each source DBMS is developed to translate their DML into SQL, which is chosen to be its kernel database language, for its user-friendliness, standardization and popularity in the industry.

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4

Rajan, S. D. "TEQUEL: an engineering relational DBMS." Engineering Computations 2, no. 3 (March 1985): 195–202. http://dx.doi.org/10.1108/eb023619.

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Coşkun, İ. B., S. Sertok, and B. Anbaroğlu. "K-NEAREST NEIGHBOUR QUERY PERFORMANCE ANALYSES ON A LARGE SCALE TAXI DATASET: POSTGRESQL VS. MONGODB." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1531–38. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1531-2019.

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<p><strong>Abstract.</strong> The increasing volume of transport network data necessitates the use of a DataBase Management System (DBMS) to store, query and analyse data. There are two main types of DBMS: relational and non-relational. Many different DBMS are available on the market but only some of them could handle spatial data. Therefore, determining which DBMS to use for operational purposes is of interest to researchers and analysts working in spatial information science. One of the commonly used spatial queries in GIS is the k-Nearest Neighbour (kNN) of a given point. This paper analyses the performance of the kNN query in PostgreSQL and MongoDB, both being a representative of relational and NoSQL DBMS respectively. Two different metrics have been investigated to determine the performance: i) spatial accuracy and ii) run time. Haversine and Vincenty formulas are used to calculate the distance between the point and the determined neighbours, which are then used to determine the spatial accuracy of the DBMS. Sensitivity analysis have been carried out by varying the k value and the execution times are recorded. The experiments are carried out on New York City’s openly available taxi dataset consisting of millions of taxi pickup and dropoff points. The results indicate that MongoDB outperforms Postgres both in terms of execution time and spatial accuracy regardless the value of k. In order to facilitate reproducibility of the results, the developed software is shared on GitHub.</p>

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Huy, Nguyen Xuan. "A labelling operator and relational operations." Journal of Computer Science and Cybernetics 1, no. 3 (August 6, 2015): 1–7. http://dx.doi.org/10.15625/1813-9663/1/3/6687.

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7

FERREIRA, RENATO, TAHSIN KURC, MICHAEL BEYNON, CHIALIN CHANG, ALAN SUSSMAN, and JOEL SALTZ. "OBJECT-RELATIONAL QUERIES INTO MULTIDIMENSIONAL DATABASES WITH THE ACTIVE DATA REPOSITORY." Parallel Processing Letters 09, no. 02 (June 1999): 173–95. http://dx.doi.org/10.1142/s0129626499000190.

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As computational power and storage capacity increase, processing and analyzing large volumes of multi-dimensional datasets play an increasingly important role in many domains of scientific research. Scientific applications that make use of very large scientific datasets have several important characteristics: datasets consist of complex data and are usually multi-dimensional; applications usually retrieve a subset of all the data available in the dataset; various application-specific operations are performed on the data items retrieved. Such applications can be supported by object-relational database management systems (OR-DBMSs). In addition to providing functionality to define new complex datatypes and user-defined functions, an OR-DBMS for scientific datasets should contain runtime support that will provide optimized storage for very large datasets and an execution environment for user-defined functions involving expensive operations. In this paper we describe an infrastructure, the Active Data Repository (ADR), which provides framework for building databases that enables integration of storage, retrieval and processing of multi-dimensional datasets on a parallel machine. The system architecture of ADR provides the functionality required from runtime support for an OR-DBMS that stores and processes scientific multi-dimensional datasets. We present the system architecture of the ADR, and experimental performance results for three applications implemented using ADR.

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Agrawal, Nishant, Anubhav Manan, Akash Aggrawal, and Rashmi Sharma. "Handling of Fuzzy Queries using Relational DBMS." International Journal of Computer Applications 68, no. 22 (April 18, 2013): 34–40. http://dx.doi.org/10.5120/11714-7365.

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9

Kramorenko, R. P., and N. A. Polyakovskaya. "Using logical derivation rules in relational DBMS." Cybernetics and Systems Analysis 33, no. 3 (May 1997): 345–53. http://dx.doi.org/10.1007/bf02733068.

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10

Aleksic, Slavica, Ivan Lukovic, Pavle Mogin, and Miro Govedarica. "A generator of SQL schema specifications." Computer Science and Information Systems 4, no. 2 (2007): 81–100. http://dx.doi.org/10.2298/csis0702081a.

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IIS*Case is an integrated CASE tool that supports the automation and intelligent support of complex and highly formalized design and programming tasks in the development of an information system. IIS*Case, as a tool from the class of domain oriented design environments, generates relational database schemas in 3rd normal form with all relevant data constraints. SQL Generator is an IIS*Case tool that generates the implementation specification of a database schema according to ANSI SQL:2003 standard. The generator may also produce a database schema specification for Microsoft SQL Server or Oracle DBMSs. The paper describes SQL Generator's traits, considers aspects of its application, and shows its use in the implementation of a complex database constraint using procedural mechanisms of a particular relational DBMS. SQL Generator is implemented in Java and Oracle JDeveloper environment.

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Amornsinlaphachai, Pensri, Nick Rossiter, and Akhtar Ali. "Storing Linked XML documents in Object-Relational DBMS." Journal of Computing and Information Technology 14, no. 3 (2006): 225. http://dx.doi.org/10.2498/cit.2006.03.06.

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Mooney, H. P., and J. W. Evans. "A complete relational DBMS for an EMS product." IEEE Transactions on Power Systems 3, no. 1 (1988): 325–29. http://dx.doi.org/10.1109/59.43218.

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Apers, P. M. G., C. A. van den Berg, J. Flokstra, P. W. P. J. Grefen, M. L. Kersten, and A. N. Wilschut. "PRISMA/DB: a parallel, main memory relational DBMS." IEEE Transactions on Knowledge and Data Engineering 4, no. 6 (1992): 541–54. http://dx.doi.org/10.1109/69.180605.

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14

Haberhauer, Franz. "Physical database design aspects of relational dbms implementations." Information Systems 15, no. 3 (January 1990): 375–89. http://dx.doi.org/10.1016/0306-4379(90)90008-d.

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15

Kawai, Yusuke, Jing Zhao, Kento Sugiura, Yoshiharu Ishikawa, and Yukiko Wakita. "An Analysis Technique of Evacuation Simulation Using an Array DBMS." Journal of Disaster Research 13, no. 2 (March 19, 2018): 338–46. http://dx.doi.org/10.20965/jdr.2018.p0338.

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Today, large-scale simulations are thriving because of the increase of computating performance and storage capacity. Understanding the results of these simulations is not easy, and hence, support for interactive and exploratory analysis is becoming more important. This study focuses on spatio-temporal simulations and attempts to develop an analysis technology to support them. It uses a database system for supporting interactive analysis of large-scale data. Since the data gained via spatio-temporal simulations is not suitable for management in a relational DBMS (RDBMS), this study uses an array DBMS, a type of DBMS that has been garnering increased attention in recent years. An array DBMS is designed for the management of large-scale array data; it provides a logical model for array data, yet it also supports efficient query processing. SciDB is used as our specific array DBMS in this paper. This study targets disaster evacuation simulation data and demonstrates via experimentation that the query-processing functions offered by an array DBMS provide effective analysis support.

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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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17

Győrödi, Cornelia A., Diana V. Dumşe-Burescu, Robert Ş. Győrödi, Doina R. Zmaranda, Livia Bandici, and Daniela E. Popescu. "Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases." Applied Sciences 11, no. 15 (July 23, 2021): 6794. http://dx.doi.org/10.3390/app11156794.

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Databases are an important part of today’s applications where large amounts of data need to be stored, processed, and accessed quickly. One of the important criteria when choosing to use a database technology is its data processing performance. In this paper, some methods for optimizing the database structure and queries were applied on two popular open-source database management systems: MySQL as a relational DBMS, and document-based MySQL as a non-relational DBMS. The main objective of this paper was to conduct a comparative analysis of the impact that the proposed optimization methods have on each specific DBMS when carrying out CRUD (CREATE, READ, UPDATE, DELETE) requests. To perform the analysis and performance evaluation of CRUD operations for different amounts of data, a case study testing architecture based on Java was developed and used to show how the databases’ proposed optimization methods can influence the performance of the application, and to highlight the differences in response time and complexity. The results obtained show the degree to which the proposed optimization methods contributed to the application’s performance improvement in the case of both databases; based on these, a detailed analysis and several conclusions are presented to support a decision for choosing a specific approach.

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18

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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Yongzhong He. "Multi-copy Decomposition for Multilevel Secure Object-relational DBMS." International Journal of Digital Content Technology and its Applications 6, no. 16 (September 30, 2012): 315–22. http://dx.doi.org/10.4156/jdcta.vol6.issue16.38.

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20

Park, Uchang. "SQL Based Graph Pattern Query Performance on Relational DBMS." Journal of Korean Institute of Information Technology 17, no. 4 (April 30, 2019): 9–20. http://dx.doi.org/10.14801/jkiit.2019.17.4.9.

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21

Cesarini, F. "Design and Implementation of a Relational DBMS for Microcomputers." Computer Journal 28, no. 4 (April 1, 1985): 391–97. http://dx.doi.org/10.1093/comjnl/28.4.391.

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Pirahesh, H., B. Mitschang, N. Südkamp, and B. Lindsay. "Composite-Object views in relational DBMS: An implementation perspective." Information Systems 19, no. 1 (January 1994): 69–88. http://dx.doi.org/10.1016/0306-4379(94)90027-2.

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23

Lee, Bum-Suk, and Byung-Yeon Hwang. "k-Bitmap Clustering Method for XML Data based on Relational DBMS." KIPS Transactions:PartD 16D, no. 6 (December 31, 2009): 845–50. http://dx.doi.org/10.3745/kipstd.2009.16d.6.845.

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24

Yuferov, Anatoliy G. "Infological models of the ENDF-format nuclear data." Nuclear Energy and Technology 5, no. 1 (March 20, 2019): 53–59. http://dx.doi.org/10.3897/nucet.5.33984.

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Issues involved in the infologic modeling of the ENDF-format nuclear data libraries for the purpose of converting ENDF files into a relational database have been considered. The transfer to a relational format will make it possible to use standard readily available tools for nuclear data processing which simplify the conversion and operation of this data array. Infological models have been described using formulas of the “Entity (List of Attributes)” type. The proposed infological formulas are based on the physical nature of data and theoretical relations. This eliminates the need for a special notation to be introduced to describe the structure and the content of data, which, in turn, facilitates the use of relational formats in codes and solution of nuclear data evaluation problems. The concept of nuclear informatics has been formulated based on relational DBMS technologies as one of the tools for solving the “big data” problem in modern science and technology. The organizational and technological grounds for the transfer of ENDF libraries to a relational format are presented. Requirements to the nuclear data presentation formats supported by relational DBMS are listed. Peculiarities of the infological model construction, conditioned by the hierarchical nature of nuclear data, are identified. The sequence for the ENDF metadata saving is presented, which can be useful for the verification and validation (testing of the structural and syntactical validity and operability) of both source data and the procedures for the conversion to a relational format. Formulas of infological models are presented for the cross sections file, the secondary neutron energy distributions file, and the nuclear reaction product energy-angle distributions file. A complete array of infological models for ENDF libraries and the generation modules of respective relational tables are available on a public website.

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Clifford, James, Albert Croker, and Alexander Tuzhilin. "On Data Representation and Use in a Temporal Relational DBMS." Information Systems Research 7, no. 3 (September 1996): 308–27. http://dx.doi.org/10.1287/isre.7.3.308.

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Ordonez, C. "Integrating K-means clustering with a relational DBMS using SQL." IEEE Transactions on Knowledge and Data Engineering 18, no. 2 (February 2006): 188–201. http://dx.doi.org/10.1109/tkde.2006.31.

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27

Vidakovic, Jovana, Sonja Ristic, Slavica Kordic, and Ivan Lukovic. "Extended tuple constraint type as a complex integrity constraint type in XML data model - definition and enforcement." Computer Science and Information Systems 15, no. 3 (2018): 821–43. http://dx.doi.org/10.2298/csis180324029v.

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A database management system (DBMS) is based on a data model whose concepts are used to express a database schema. Each data model has a specific set of integrity constraint types. There are integrity constraint types, such as key constraint, unique constraint and foreign key constraint that are supported by most DBMSs. Other, more complex constraint types are difficult to express and enforce and are mostly completely disregarded by actual DBMSs. The users have to manage those using custom procedures or triggers. eXtended Markup Language (XML) has become the universal format for representing and exchanging data. Very often XML data are generated from relational databases and exported to a target application or another database. In this context, integrity constraints play the essential role in preserving the original semantics of data. Integrity constraints have been extensively studied in the relational data model. Mechanisms provided by XML schema languages rely on a simple form of constraints that is sufficient neither for expressing semantic constraints commonly found in databases nor for expressing more complex constraints induced by the business rules of the system under study. In this paper we present a classification of constraint types in relational data model, discuss possible declarative mechanisms for their specification and enforcement in the XML data model, and illustrate our approach to the definition and enforcement of complex constraint types in the XML data model on the example of extended tuple constraint type.

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Frénot, S., and F. Laforest. "Medical Record Management Systems: Criticisms and New Perspectives." Methods of Information in Medicine 38, no. 02 (1999): 89–95. http://dx.doi.org/10.1055/s-0038-1634179.

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AbstractThe first generation of computerized medical records stored the data as text, but these records did not bring any improvement in information manipulation. The use of a relational database management system (DBMS) has largely solved this problem as it allows for data requests by using SQL. However, this requires data structuring which is not very appropriate to medicine. Moreover, the use of templates and icon user interfaces has introduced a deviation from the paper-based record (still existing). The arrival of hypertext user interfaces has proven to be of interest to fill the gap between the paper-based medical record and its electronic version. We think that further improvement can be accomplished by using a fully document-based system. We present the architecture, advantages and disadvantages of classical DBMS-based and Web/DBMS-based solutions. We also present a document-based solution and explain its advantages, which include communication, security, flexibility and genericity.

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Berski, Szymon, and Martyna Bilau. "Safety Mechanisms in Relational Database as Part of the IT System of the Enterprise." New Trends in Production Engineering 2, no. 2 (December 1, 2019): 12–23. http://dx.doi.org/10.2478/ntpe-2019-0068.

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Abstract The work examines the effectiveness of the replication mechanism of the X production company database. In order to extend the functionality of the computer database of the enterprise, a model of its resource flow was created and an intensional and extensional part was created for a test database based on real enterprise resources. The model has been simplified to 3-rd normal form. The implementation was done in the MySQL database system. Two different operating systems were selected for testing: Windows and Linux. The database management system (DBMS) is working under the GPL license. MySQL DBMS offers many security mechanisms, and to secure the database, system of users permissions to objects have been selected and also an encryption of access passwords for users and connections to servers was used. A replication engine based on the binary log and the model “main server and backup server” was used to create a copy of the enterprise data.

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ANDERSSON, MARTIN. "EXTRACTING AN ENTITY RELATIONSHIP SCHEMA FROM A RELATIONAL DATABASE THROUGH REVERSE ENGINEERING." International Journal of Cooperative Information Systems 04, no. 02n03 (June 1995): 259–85. http://dx.doi.org/10.1142/s0218843095000111.

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This article presents a method for extracting a conceptual schema from a relational database. The method is based on an analysis of data manipulation statements in the code of an application using a relational DBMS. Attributes representing references between tables in the relational schema and possible keys are determined by an analysis of join conditions in queries and view definitions. Knowledge about which attributes link tables is used to investigate the database extension in a selective manner. When the keys cannot be unambiguously determined, possible solutions are generated by the system under guidance of the user. The approach makes it possible to efficiently construct a conceptual schema from only rudimentary information.

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Andrianov, Andrei, Vladimir Ilyushechkin, Pavel Chumachenko, and Elena Fedotova. "REVIEW OF NON-RELATIONAL DBMS VIKTA PC ARCHITECTURE AND ORGANIZATION PRINCIPLES." Proceedings of Irkutsk State Technical University 20, no. 12 (December 2016): 91–100. http://dx.doi.org/10.21285/1814-3520-2016-12-91-100.

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Benabbou, Amel, and Safia Nait Bahloul. "Specification-based Approach for Denotational Semantic of Orthogonal Object/Relational DBMS." Procedia Computer Science 31 (2014): 369–78. http://dx.doi.org/10.1016/j.procs.2014.05.280.

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Manyam, Ganiraju, Michelle A. Payton, Jack A. Roth, Lynne V. Abruzzo, and Kevin R. Coombes. "Relax with CouchDB — Into the non-relational DBMS era of bioinformatics." Genomics 100, no. 1 (July 2012): 1–7. http://dx.doi.org/10.1016/j.ygeno.2012.05.006.

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Kumar Barai, Dilip, G. Sridevi, Syed Umar, and MSR Prasad. "Information Base Security Threats and Challenges' in Information Forensic: A Survey." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 2 (February 1, 2016): 406. http://dx.doi.org/10.11591/ijeecs.v1.i2.pp406-410.

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<p>Generally to store the information or Information of any organization then they will be maintain Information base to manage that Information base we use some management techniques like Information base management systems so called DBMS. In this paper we introduced Relational DBMS which is a collection of applications that can store various information which can be easily retrieve, manipulate and storage of Information. So in this we are concentrating on forensic analysis and Information base of it which is very sensitive Information. In this paper we are analyzing and surveying of forensic Information based using various methodologies with different tools and algorithms for investigations, through which we got what are the challenges are facing in the forensic Information bases since the years.</p>

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Liu, Chen, Junghyun Ko, and Jeongmo Yeo. "Performance Comparison of DW System Tajo Based on Hadoop and Relational DBMS." KIPS Transactions on Software and Data Engineering 3, no. 9 (September 30, 2014): 349–54. http://dx.doi.org/10.3745/ktsde.2014.3.9.349.

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Jaedicke, Michael, and Bernhard Mitschang. "On parallel processing of aggregate and scalar functions in object-relational DBMS." ACM SIGMOD Record 27, no. 2 (June 1998): 379–89. http://dx.doi.org/10.1145/276305.276338.

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Moertini, Veronica S., Benhard Sitohang, and Oerip S. Santosa. "INTEGRASI ALGORITMA POHON KEPUTUSAN C4.5 YANG DIKEMBANGKAN KE DALAM OBJECT-RELATIONAL DBMS." JUTI: Jurnal Ilmiah Teknologi Informasi 6, no. 2 (July 1, 2007): 39. http://dx.doi.org/10.12962/j24068535.v6i2.a186.

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Fedorchenko, K. "Realization Of Methods Of Comparison Of Sets By Using Relational Dbms Methods." Information Technologies in Education, no. 3 (April 15, 2009): 279–82. http://dx.doi.org/10.14308/ite000086.

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Whang, Kyu-Young, Inju Na, Tae-Seob Yun, Jin-Ah Park, Kyu-Hyun Cho, Se-Jin Kim, Ilyeop Yi, and Byung Suk Lee. "Building social networking services systems using the relational shared-nothing parallel DBMS." Data & Knowledge Engineering 125 (January 2020): 101756. http://dx.doi.org/10.1016/j.datak.2019.101756.

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Gromey, Dmitry Dmitrievich. "ALGORITHMS FOR MANAGING THE LOGICAL STRUCTURE OF THE DATABASE USING A PARAMETRIC MODEL OF COMPETITIVE ACCESS TO QUERIES BASED ON THE RANDOM FOREST METHOD." Computational nanotechnology 6, no. 2 (June 30, 2019): 41–47. http://dx.doi.org/10.33693/2313-223x-2019-6-2-41-47.

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In article discusses the approach to development of mathematical software for support the process of managing the data schema in relational DBMS in terms of processing of parallel queries stream that compete for data in the hierarchy of the DBMS core memory. The necessity of the formation of a parametric model of queries competitive access. Briefly discusses methods of machine learning, allowing to solve the problem of regression recovery. The use of the random forest method as the most universal method of approximation of arbitrary functions is substantiated. A method of forming a parametric model of competitive access based on the random forest method, as well as an approach with the ensemble of sets of decision trees, which allows to provide the required generalizing ability and stability of the model to partial features and diversity of all types of queries received at the input of the DBMS. The stages of the developed algorithms are presented: ranking query parameters by total execution time and automatic data distribution, allowing you to go from approximating the target system with linear-continuous functions to a set of logical data schema objects, ordered by their effect on time, total query execution time, reducing multi-criteria optimization task to a task optimization by one criterion.

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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. 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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N. Gedam, Madhuri, and B. B. Meshram. "Database Private Security Jurisprudence: A Case Study using Oracle." International Journal of Database Management Systems 13, no. 03 (June 30, 2021): 01–21. http://dx.doi.org/10.5121/ijdms.2021.13301.

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Oracle is one of the largest vendors and the best DBMS solution of Object Relational DBMS in the IT world. Oracle Database is one of the three market-leading database technologies, along with Microsoft SQL Server's Database and IBM's DB2. Hence in this paper, we have tried to answer the million-dollar question “What is user’s responsibility to harden the oracle database for its security?” This paper gives practical guidelines for hardening the oracle database, so that attacker will be prevented to get access into the database. The practical lookout for protecting TNS, Accessing Remote Server and Prevention, Accessing Files on Remote Server, Fetching Environment Variables, Privileges and Authorizations, Access Control, writing security policy, Database Encryption, Oracle Data Mask, Standard built in Auditing and Fine Grained Auditing (FGA) is illustrated with SQL syntax and executed with suitable real life examples and its output is tested and verified. This structured method acts as Data Invictus wall for the attacker and protect user’s database.

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He, Zhen, Byung Suk Lee, and Robert Snapp. "Self-tuning cost modeling of user-defined functions in an object-relational DBMS." ACM Transactions on Database Systems 30, no. 3 (September 2005): 812–53. http://dx.doi.org/10.1145/1093382.1093387.

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Wang, Ming, and H. Walker. "Creation of an intelligent process planning system within the relational DBMS software environment." Computers in Industry 13, no. 3 (December 1989): 215–28. http://dx.doi.org/10.1016/0166-3615(89)90112-7.

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Diallo, B., J. M. Travere, and B. Mazoyer. "A Review of Database Management Systems Suitable for Neuroimaging." Methods of Information in Medicine 38, no. 02 (1999): 132–39. http://dx.doi.org/10.1055/s-0038-1634174.

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AbstractThis study comprises a technical assessment of Database Management Systems (DBMS), which may be of use in the analysis of data obtained from human brain mapping procedures. Due to the large expansion of the neuroimaging field, the use of specialized database software to store and process neuroimages and their attached components is inevitable. The advent of multiple software products, a wealth of technical terms and a wide variety of other applications make the choice of a suitable program sometimes difficult. Through the inclusion of some basic and pertinent criteria (e.g., performance, ease of opening, standardization and portability), we present a descriptive comparison of 12 DBMSs currently available in the commercial and public domain. We have compared and tested three main architecture models which are currently available and assessed their potential applications for imaging purposes: relational, object-oriented, and hybrid. The findings of our study demonstrated that the Illustra™ software was the best suited for a neuroimaging environment because of its intrinsic ability to handle complex and large objects, such as 3D volumes or geometric structures.

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Yuferov, Anatoliy. "Converting ENDF libraries into relational format." Nuclear Energy and Technology 4, no. 1 (October 17, 2018): 57–63. http://dx.doi.org/10.3897/nucet.4.29858.

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The article considers the issues of converting the ENDF format systems of constants to relational databases. This conversion can become one of the tools facilitating the development and operation of factual information, techniques and algorithms in the field of nuclear data and, therefore, increasing the efficiency of the corresponding computational codes. The work briefly examines an infological model of ENDF libraries. The possible structure of tables of the corresponding relational database is described. The proposed database schema and the form of tables take into account the presence of both single and multiple properties of the isotopes under consideration. Consideration is given to the difference in organizational requirements for transferring constants from relational tables to programs and performing a visual analysis of data in tables by a physicist-evaluator. The conversion algorithms and results are described for the ROSFOND-A and ENDF/B-VII.1 libraries. It is shown that performing calculations directly in the DBMS environment has its advantages in terms of simplifying programming and eliminating the need to solve a number of problems on data verification and validation. Possible approaches are indicated to ensure operation of inherited software together with nuclear data libraries in the relational format. Some terminological refinements are proposed to facilitate constructing an infological model for ENDF format. The conversion programs and the ENDF/B-VII.1 library in the relational format are available on a public site.

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Michalewicz, Zbigniew, and Alvin Yeo. "A Good Normal Form for Relational Databases." Fundamenta Informaticae 12, no. 2 (April 1, 1989): 129–38. http://dx.doi.org/10.3233/fi-1989-12202.

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In the conceptual design of relational databases one of the main goals is to create a conceptual scheme, which minimize redundancies and eliminate deletion and addition anomalies, i.e. to create relation schemes in some good normal form. The study of relational databases has produced a host of normal forms: 2NF, 3NF, BCNF, Elementary-Key Normal Form, 4NF, Weak 4NF, PJ/NF, DK/NF, LTKNF, (3,3)NF, etc. There are two features which characterize these normal forms. First, they consider each relation separately. We believe that a normal form (which reflects the goodness of the conceptual design) should be related to the whole conceptual scheme. Second, the usefullness of all normal forms in relational database design have been based on the assumption that a data definition language (DDL) of a database management system (DBMS) is able to enforce key dependencies. However, different DDLs have different capabilities in defining constraints. In this paper we will discuss the design of conceptual relational schemes in general. We will also define a good normal form (GNF) which requires a minimally rich DDL; this normal form is based only on a primitive concept of constraints. We will not, however, discuss the normalization process itself – how one might, if possible, convert a relation scheme that is not in some normal form into a collection of relation schemes each of which is in that normal form.

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Dignös, Anton, Michael H. Böhlen, Johann Gamper, and Christian S. Jensen. "Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal Queries." ACM Transactions on Database Systems 41, no. 4 (December 23, 2016): 1–46. http://dx.doi.org/10.1145/2967608.

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Li, Hang, Hua Liu, Yong Liu, and Yuan Wang. "AN OBJECT-RELATIONAL IFC STORAGE MODEL BASED ON ORACLE DATABASE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 625–31. http://dx.doi.org/10.5194/isprsarchives-xli-b2-625-2016.

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With the building models are getting increasingly complicated, the levels of collaboration across professionals attract more attention in the architecture, engineering and construction (AEC) industry. In order to adapt the change, buildingSMART developed Industry Foundation Classes (IFC) to facilitate the interoperability between software platforms. However, IFC data are currently shared in the form of text file, which is defective. In this paper, considering the object-based inheritance hierarchy of IFC and the storage features of different database management systems (DBMS), we propose a novel object-relational storage model that uses Oracle database to store IFC data. Firstly, establish the mapping rules between data types in IFC specification and Oracle database. Secondly, design the IFC database according to the relationships among IFC entities. Thirdly, parse the IFC file and extract IFC data. And lastly, store IFC data into corresponding tables in IFC database. In experiment, three different building models are selected to demonstrate the effectiveness of our storage model. The comparison of experimental statistics proves that IFC data are lossless during data exchange.

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Li, Hang, Hua Liu, Yong Liu, and Yuan Wang. "AN OBJECT-RELATIONAL IFC STORAGE MODEL BASED ON ORACLE DATABASE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 625–31. http://dx.doi.org/10.5194/isprs-archives-xli-b2-625-2016.

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With the building models are getting increasingly complicated, the levels of collaboration across professionals attract more attention in the architecture, engineering and construction (AEC) industry. In order to adapt the change, buildingSMART developed Industry Foundation Classes (IFC) to facilitate the interoperability between software platforms. However, IFC data are currently shared in the form of text file, which is defective. In this paper, considering the object-based inheritance hierarchy of IFC and the storage features of different database management systems (DBMS), we propose a novel object-relational storage model that uses Oracle database to store IFC data. Firstly, establish the mapping rules between data types in IFC specification and Oracle database. Secondly, design the IFC database according to the relationships among IFC entities. Thirdly, parse the IFC file and extract IFC data. And lastly, store IFC data into corresponding tables in IFC database. In experiment, three different building models are selected to demonstrate the effectiveness of our storage model. The comparison of experimental statistics proves that IFC data are lossless during data exchange.

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