Theory of bioinspired search for optimal solutions and its application for the processing of problem-oriented knowledge

Rodzin, Sergey; Rodzina, Lada

doi:10.1109/icaict.2014.7035930

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Currently, the problem of big data processing is one of the most urgent problems in the field of information technology; moreover, it generates the most serious problems of an algorithmic nature associated with ensuring the accuracy and computational efficiency of the processing processes [4]. Analysis of the state of research and development on formulated problems shows that existing methods and algorithms for big data processing do not meet the expectations and needs of specialists in this field and determine the relevance of the work [2][3][4][5]. The paper suggests an approach to the integrated processing of unstructured data of large volume, based on methods of evolutionary modeling, semantic and cluster analysis of unstructured data.…”

Section: Problem Statementmentioning

confidence: 99%

“…To solve it, many algorithms have been developed [2,[5][6][7], and in recent years, within the framework of the Big Data concept, special attention has been paid to the processing of information stored in either very large databases (VLDB) or for on-line processing in the form of a data stream [1]. To solve these problems, the mathematical apparatus of computational intelligence [4][5][6] and soft computing can be successfully used.…”

Section: Problem Statementmentioning

confidence: 99%

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Multi-level Ontological Model of Big Data Processing

Bova

Kureichik

Scheglov

et al. 2018

Advances in Intelligent Systems and Computing

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The paper presents a possible solution to the problems of structuring data of a large volume, as well as their integrated storage in structures that ensure the integrity, consistency of their presentation, high speed and flexibility of processing unstructured information. To solve mentioned problems, the authors propose a method for developing a multi-level ontological structure that provides a solution to interrelated problems of identifying, structuring and processing big data sets that has primarily natural-linguistic forms of representation. This multi-level model is developed based on methods of semantic analysis and relative modeling. The model is suitable for the interpretation and effective integrated processing of unstructured data obtained from distributed sources of information. The multilevel representation of the big data determines the methods and mechanisms of the unified meta-description of the data elements at the logical level, the search for patterns and classification of the characteristic space at the semantic level, and the linguistic level of the procedures for identifying, consolidating and enriching data. The modification of this method consists in applying a scalable and computationally effective genetic algorithm for searching and generating weight coefficients that correspond to different similarity measures for the set of observed features used in the dataclustering model.

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Section: Problem Statementmentioning

confidence: 99%