Development of complex methodology of processing heterogeneous data in intelligent decision support systems

• Main Authors: Pavlo Zuiev, Ruslan Zhyvotovskyi, Oleksii Zvieriev, Serhiy Hatsenko, Volodymyr Kuprii, Oleksandr Nakonechnyi, Mykhailo Adamenko, Andrii Shyshatskyi, Yevhenii Neroznak, Vira Velychko
• Format: Article
• Language: English
• Published: PC Technology Center 2020-08-01
• Series: Eastern-European Journal of Enterprise Technologies
• Subjects: decision support system; monitoring object; different types of data; computational complexity; information processing; type of information
• Online Access: http://journals.uran.ua/eejet/article/view/208554
• ISSN: 1729-3774; 1729-4061

The complex methodology for processing heterogeneous data in intelligent decision support systems is developed. This method is made to increase the efficiency of processing heterogeneous data in intelligent decision support systems. The complex methodology consists of the following interrelated procedures: heterogeneous data storing model; heterogeneous data synchronization algorithm; heterogeneous data separation algorithm; heterogeneous data indexing algorithm. The model of storing heterogeneous intelligence data, which is the basis of the methodology, differs in the presence of templates of intelligence objects and parameter templates of intelligence objects. Templates allow storing both unstructured heterogeneous intelligence data and structured intelligence data according to a defined pattern, which reduces the time to access the data. In the heterogeneous intelligence data storage model, a heterogeneous intelligence data synchronization algorithm, heterogeneous intelligence data separation algorithm and heterogeneous intelligence data indexing algorithm are developed. The development of the proposed technique is due to the need to increase the efficiency of processing various information types in intelligent decision support systems with acceptable computational complexity. The proposed method allows increasing the efficiency of intelligent decision support systems through integrated processing of data circulating in them. The proposed method allows increasing the efficiency of information processing in decision support systems from 16 to 20 % depending on the amount of information about the monitoring object