DATA MINING IN RELATIONAL SYSTEMS
The subject of the research is methods of relational database mining. The purpose of the research is to develop scientifically grounded models for supporting intelligent technologies for integrating and managing information resources of distributed computing systems. Explore the features of the oper...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Kharkiv National University of Radio Electronics
2020-09-01
|
| Series: | Сучасний стан наукових досліджень та технологій в промисловості |
| Subjects: | |
| Online Access: | https://itssi-journal.com/index.php/ittsi/article/view/217 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849248903361200128 |
|---|---|
| author | Valentin Filatov Valerii Semenets Oleg Zolotukhin |
| author_facet | Valentin Filatov Valerii Semenets Oleg Zolotukhin |
| author_sort | Valentin Filatov |
| collection | DOAJ |
| description | The subject of the research is methods of relational database mining. The purpose of the research is to develop scientifically grounded models for supporting intelligent technologies for integrating and managing information resources of distributed computing systems. Explore the features of the operational specification of the relational data model. To develop a method for evaluating a relational data model and a procedure for constructing functional associative rules when solving problems of mining relational databases. In accordance with the set research goal, the presented article considers the following tasks: analysis of existing methods and technologies for data mining. Research of methods for representing intelligent models by means of relational systems. Development of technology for evaluating the relational data model for building functional association rules in the tasks of mining relational databases. Development of design tools and maintenance of applied data mining tasks; development of applied problems of data mining. Results: The analysis of existing methods and technologies for data mining is carried out. The features of the structural specification of a relational database, the formation of association rules for building a decision support system are investigated. Information technology has been developed, a methodology for the design of information and analytical systems, based on the relational data model, for solving practical problems of mining, practical recommendations have been developed for the use of a relational data model for building functional association rules in problems of mining relational databases, conclusion: the main source of knowledge for database operation can be a relational database. In this regard, the study of data properties is an urgent task in the construction of systems of association rules. On the one hand, associative rules are close to logical models, which makes it possible to organize efficient inference procedures on them, and on the other hand, they more clearly reflect knowledge than classical models. They do not have the strict limitations typical of logical calculus, which makes it possible to change the interpretation of product elements. The search for association rules is far from a trivial task, as it might seem at first glance. One of the problems is the algorithmic complexity of finding frequently occurring itemsets, since as the number of items grows, the number of potential itemsets grows exponentially. |
| format | Article |
| id | doaj-art-d5bd9eae679d4eecae95b2fe15b4cc4c |
| institution | Kabale University |
| issn | 2522-9818 2524-2296 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | Kharkiv National University of Radio Electronics |
| record_format | Article |
| series | Сучасний стан наукових досліджень та технологій в промисловості |
| spelling | doaj-art-d5bd9eae679d4eecae95b2fe15b4cc4c2025-08-20T03:57:43ZengKharkiv National University of Radio ElectronicsСучасний стан наукових досліджень та технологій в промисловості2522-98182524-22962020-09-013 (13)10.30837/ITSSI.2020.13.076DATA MINING IN RELATIONAL SYSTEMSValentin Filatov0Valerii Semenets1Oleg Zolotukhin2Kharkov National University of Radio ElectronicsKharkov National University of Radio ElectronicsKharkov National University of Radio ElectronicsThe subject of the research is methods of relational database mining. The purpose of the research is to develop scientifically grounded models for supporting intelligent technologies for integrating and managing information resources of distributed computing systems. Explore the features of the operational specification of the relational data model. To develop a method for evaluating a relational data model and a procedure for constructing functional associative rules when solving problems of mining relational databases. In accordance with the set research goal, the presented article considers the following tasks: analysis of existing methods and technologies for data mining. Research of methods for representing intelligent models by means of relational systems. Development of technology for evaluating the relational data model for building functional association rules in the tasks of mining relational databases. Development of design tools and maintenance of applied data mining tasks; development of applied problems of data mining. Results: The analysis of existing methods and technologies for data mining is carried out. The features of the structural specification of a relational database, the formation of association rules for building a decision support system are investigated. Information technology has been developed, a methodology for the design of information and analytical systems, based on the relational data model, for solving practical problems of mining, practical recommendations have been developed for the use of a relational data model for building functional association rules in problems of mining relational databases, conclusion: the main source of knowledge for database operation can be a relational database. In this regard, the study of data properties is an urgent task in the construction of systems of association rules. On the one hand, associative rules are close to logical models, which makes it possible to organize efficient inference procedures on them, and on the other hand, they more clearly reflect knowledge than classical models. They do not have the strict limitations typical of logical calculus, which makes it possible to change the interpretation of product elements. The search for association rules is far from a trivial task, as it might seem at first glance. One of the problems is the algorithmic complexity of finding frequently occurring itemsets, since as the number of items grows, the number of potential itemsets grows exponentially.https://itssi-journal.com/index.php/ittsi/article/view/217information systemdatabaserelational data modeldata integrationintelligent systemsextracting knowledge from data |
| spellingShingle | Valentin Filatov Valerii Semenets Oleg Zolotukhin DATA MINING IN RELATIONAL SYSTEMS Сучасний стан наукових досліджень та технологій в промисловості information system database relational data model data integration intelligent systems extracting knowledge from data |
| title | DATA MINING IN RELATIONAL SYSTEMS |
| title_full | DATA MINING IN RELATIONAL SYSTEMS |
| title_fullStr | DATA MINING IN RELATIONAL SYSTEMS |
| title_full_unstemmed | DATA MINING IN RELATIONAL SYSTEMS |
| title_short | DATA MINING IN RELATIONAL SYSTEMS |
| title_sort | data mining in relational systems |
| topic | information system database relational data model data integration intelligent systems extracting knowledge from data |
| url | https://itssi-journal.com/index.php/ittsi/article/view/217 |
| work_keys_str_mv | AT valentinfilatov datamininginrelationalsystems AT valeriisemenets datamininginrelationalsystems AT olegzolotukhin datamininginrelationalsystems |