In the rapidly evolving landscape of data-driven decision-making, a groundbreaking study led by Valentin Filatov from Kharkiv National University of Radio Electronics is set to revolutionize how industries, particularly the energy sector, harness the power of relational databases. Published in the journal ‘Сучасний стан наукових досліджень та технологій в промисловості’ (Modern State of Scientific Research and Technologies in Industry), Filatov’s research delves into the intricate world of intellectual data analysis within relational information and analytical systems.
At the heart of Filatov’s work lies the exploration of methods for constructing decision trees, associative analysis, and identifying patterns between related events using data presented by a relational model. “The relational data model is considered the most effective data structure used in intelligent information systems for data processing and storage,” Filatov explains. This model’s efficiency is attributed to its ability to build logical dependencies between information units within a subject area, a critical factor for industries dealing with vast amounts of data.
One of the key contributions of Filatov’s research is the formalization of the problem of knowledge extraction from relational databases. By identifying and analyzing a group of aggregate functions of relational databases concerning key attributes of relations, Filatov’s work paves the way for more sophisticated data mining techniques. This is particularly relevant for the energy sector, where the ability to extract meaningful insights from complex datasets can lead to more informed decision-making and improved operational efficiency.
The study also introduces the concept of functional associative rules and provides a detailed analysis of the ID3 decision tree generation algorithm, specifically tailored for data processing in relational systems. “The semantic network (SN), built on the basis of the proposed approach, allows to increase the efficiency of decision support systems,” Filatov notes. This enhancement in decision support systems can be a game-changer for the energy sector, enabling more accurate predictions and better resource management.
The implications of Filatov’s research extend beyond the energy sector, offering a universal approach to building relational data models for information systems. This approach is particularly effective in solving tasks where objects are connected by a “many-to-many” relationship or M →N. By proposing the relational database model as a universal information structure for solving associative analysis tasks and presenting knowledge in the form of a semantic network, Filatov’s work sets a new standard for data analysis and management.
As industries continue to grapple with the challenges of big data, Filatov’s research provides a beacon of hope. The examples given in the article confirm the effectiveness of the developed and considered approaches to solving the problem of data mining in the environment of relational systems. By improving the quality of management decisions made, this research has the potential to shape future developments in the field, driving innovation and efficiency across various sectors.
In an era where data is king, Filatov’s work underscores the importance of robust data structures and intelligent analysis methods. As the energy sector and other industries strive to harness the power of data, Filatov’s research offers a roadmap for navigating the complex landscape of relational databases and data mining, ultimately leading to more informed and effective decision-making.