Big Data Arrays Research Articles

Clustering of bacteria by the triplet composition of 5S RNA

The relationship between the structure of nucleotide sequences, the function they encode, and the taxonomy of the host is an important subject of study for molecular biologists and bioinformaticians, as well as specialists in processing big data arrays. There are various approaches in this direction; one of them is connected with the study of the relationship between the structure of biological macromolecules (specifically, bacterial ribosomal RNA molecules) and the taxonomy of their carriers. For these organisms, 16S RNA sequences are a classic subject of study. However, of no less interest is the study of the relationship be-tween structure and taxonomy for other sequences. In the framework of this work, we studied the relationship between structure and taxonomy using bacterial 5S RNA sequences as an example.

Консолидация информационных моделей объектов строительства в единое пространство больших данных

The article discusses the issues of consolidation and integration of the accumulated arrays of capital construction models information of objects into a common space of information models, created according to the organization and processing of big data arrays principles. The problems of geoinformation binding of information models, organization of means and places of their storage, methods of processing and providing access to interested subjects are considered. It is proposed to create a common space of information models according to a distributed decentralized scheme with direct interaction between state and commercial participants, both in terms of business integration, and in terms of joint data management and implementation of replication technologies. Examples of practical application of integrative solutions of a common information space of open data from information models of construction objects in relation to existing information systems and services are given.

Using Yield Mapping Data to Identify Areas of In-field Heterogeneity in Soil Fertility

Mathematical models developed on the basis of a big data array of stationary agrochemical field experiments of the Federal State Budgetary Scientific Institution "Federal Research Center "Nemchinovka" on different types (subtypes) of soils for calculating the potential yield and doses of fertilizers for the planned crop yield are presented. The combined use of such models with yield mapping data can potentially be used to identify areas of in-field heterogeneity in soil fertility.

МОДЕЛЮВАННЯ ЕКОНОМІЧНОГО РОСТУ МЕТОДАМИ ІНТЕЛЕКТУАЛЬНОГО АНАЛІЗУ ДАНИХ

The article substantiates the expediency of using intelligent data analysis in the study and forecasting of economic growth. Intelligent data analysis is a powerful tool that can help researchers and politicians understanding economic growth more deeply and making reasonable decisions by forming state policy. In addition, intelligent data analysis can be used to determine the potential areas of economic growth and investment opportunities. Attention is focused on using CART (predictive modeling tool, which has proved its high efficiency in understanding the non-obvious relationships between several variable statistical importance) and associative rules (intelligent data analysis, which can work with big data arrays and generate clear and effective conclusions, supported by statistical significance indicators). It is concluded that the methods of intelligent data analysis, such as CART and associative rules, are crucial for understanding the complex patterns that exist in large arrays of economic data. The advantages of these methods make them appropriate and effective tools for modeling and understanding of levers and tools of economic growth. Based on the analysis of data for 119 countries of the world for 2021 (the period of post-pandemic recovery), it was established that in order to promote economic growth, countries with low levels of export and import need to focus on the development of national industry. In addition, developing an economic policy aimed at promoting the recovery of the economy, it is necessary to increase the democratic status of the country. In general, countries that are less dependent on exports and imports tend to have more stable and self-sufficient economies. This can be explained by a number of factors, such as a strong domestic market, the development of national industry and less vulnerability to global economic shocks. In addition, a low level of imports may indicate that a country is able to produce and consume a significant portion of its own goods, which may contribute to further economic growth and development. It is important to note that these relationships are based on highlevel statistical correlations.

ПРОГНОЗУВАННЯ ЧАСОВИХ РЯДІВ СОЦІАЛЬНИХ МЕРЕЖ НА ОСНОВІ НЕЙРОМЕРЕЖЕВОГО ПІДХОДУ

The article presents a predictive model for analyzing the state and behavior of social network objects in dynamics and for building predictions of user behavior. This opens up opportunities for choosing the most promising promotion of objects in social networks, the best placement of advertising, the possibility of creating independent projects. The article proposes an approach to the formalization of the processes of growth/decrease in the popularity of content under the influence of external factors within the framework of a mathematical predictive model based on a recurrent neural network. The purpose of the model is mathematical processing and analysis of stochastic information flows, graphical presentation of forecasts of music trends, changes in the popularity of bloggers, video content, and more. The developed model can be used to predict the growth of reposts in social networks. The list of external and internal variables that affect the behavior of the output data at each moment of time is described. The behavior of time series is studied on the basis of input data obtained from social networks (nomenclature of indicators, number of reposts of researched objects, simulation period). A feature of the model is the ability to work with Big Data arrays, which in most cases have incomplete or "noisy" information. The dependence between input and output is proposed to be specified using a system of constraints and external coefficients that change over time. Control parameters are predictors of the model. Features of building a predictive model are shown, a list of the main limitations of the mathematical model is described. The result of the implementation of the tasks set in the research is a dynamic predictive model that provides an opportunity to create predictions based on such indicators as reposts on social networks, music trends, and the popularity of bloggers. It is proposed to use a recurrent artificial network with a dynamic neuron to predict reposts on selected research objects. The neuron processes input signals and calculates a predictive indicator of the number of reposts. The learning process of the neural network during the simulation period of one month is demonstrated. To reveal the prospects of further development of the model in order to improve the accuracy of forecasts of socio-economic processes.

Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems

The specificity of the water treatment field, associated with water transmission, distribution and accounting, as well as the need to use automation and intelligent tools for various information solutions and security tools, have resulted in the development of integrated approaches and practical solutions regarding various aspects of the functioning of such systems. The research problem lies in the insecurity of water treatment systems and their susceptibility to malicious influences from the side of potential intruders trying to compromise the functioning. To obtain initial data needed for assessing the states of a water treatment system, the authors have developed a case study presenting a combination of a physical model and a software simulator. The methodology proposed in the article includes combining methods of machine learning and visual data analysis to improve the detection of attacks and anomalies in water treatment systems. The selection of the methods and tuning of their modes and parameters made it possible to build a mechanism for efficient detection of attacks in data from sensors with accuracy values above 0.95 for each class of attack and mixed data. In addition, Change_Measure metric parameters were selected to ensure the detection of attacks and anomalies by using visual data analysis. The combined method allows identifying points when the functioning of the system changes, which could be used as a trigger to start resource-intensive procedures of manual and/or machine-assisted checking of the system state on the basis of the available machine learning models that involve processing big data arrays.

Comparative analysis of methods for prediction continuous numerical features on big datasets

The object of research is the process of choosing a method for predicting continuous numerical features on big datasets. The importance of the study is due to the fact that today in various subject areas it is necessary to solve the problem of predicting performance indicators based on data collected from different sources and presented in different formats, which is the task of big data analysis. To solve the problem, the methods of statistical analysis were considered, namely multiple linear regression, decision trees and a random forest. An array of extensive data was built without specifying the subject area, its preliminary processing, analysis was carried out to establish the correlation between the features. The processing of the big data array was carried out using the technology of parallel computing by means of the Dask library of the Python language. Since working with big data requires significant computing resources, this approach does not require the use of powerful computer technology. Prediction models were built using multiple linear regression methods, decision trees and a random forest, visualization of the prediction results and analysis of the reliability of the constructed models. Based on the results of calculating the prediction error, it was found that the greatest prediction accuracy among the considered methods is the random forest method. When applying this method, the prediction accuracy for a dataset of numerical features was approximately 97 %, which indicates a high reliability of the constructed model. Thus, it is possible to conclude that the random forest method is suitable for solving prediction problems using large data sets, it can be used for datasets with a large number of features and is not sensitive to data scaling. The developed software application in Python can be used to predict numerical features from different subject areas, the prediction results are imported into a text file.

Comparison of SEM-Assisted Nanoporometric and Microporometric Morphometric Techniques Applied for the Ultramicroporous Polymer Films

One of the most important applications of polymeric porous nanomaterials is the design of nanoporous structures for operation in patch-clamp systems allowing to establish a gigaohm contact, as well as for the measurements of biomolecules, informational macromolecules, including DNA, translocating through the nanopore arrays. Development of nanopore sequencing techniques leads to fundamentally new big data arrays, but their representativeness and validity, as well as the validity of counting of biomacromolecular particles based on ultramicropore arrays, strongly depends both on the pore size (in engineering lithography unimodal pore size distribution is optimal) and the accuracy of the size distribution measurements using instrumental methods. However, the former is unattainable when using soft matter or stretchable, plastic and elastic polymer materials and films, while the latter depends on the metrological parameters of the instrumental and algorithmic porosimetry techniques. Therefore in this paper the question about the applicability of polymer materials with pore arrays for the studies of biomacromolecules and bionanostructures is proposed to be answered using a comparative analysis of two different porosimetry approaches with the resolution not lower than electron microscopic one.

A model of cryptographic network protection when using distributed Big Data arrays

Big Data arrays are used when analysing the accumulated information and identifying patterns that can be implemented in the form of documents or development strategies, depending on the type of object. The use of the Big Data analysis methodology makes it possible to assert that the formation of clusters for storing and using the obtained information is possible only with an active correlation and interaction between individual arrays. The novelty of the study is determined by the fact that the use of Big Data in the tasks of socio-economic development requires the simultaneous analysis of information from various institutions and establishments. The authors show that Big Data analysis for the purposes of socio-economic development is possible only if access to distributed networks is established. At the same time, network protection should be based on closed cryptographic protocols. The paper shows that the use of protocols of a cryptographic type also makes it possible to verify the received data. The practical significance of the study is determined by the structure of a distributed type network and formation of a model for using Big Data in the tasks of socio-economic development. This will allow in the long term to ensure the establishment of a civil society model and reduce both financial and credibility losses.

Bibliologos – the vaccine from ignorance and nonsense

The neologism “Bibliologos” is literally translated from Greek as a “Book mind”. Real areas of bibliologos existence are education, science, management. Bibliologos can be considered from three points of view: first, genetic (or rather epistemological, theoretical and cognitive) measurement, revealing the initial substances and philosophical categories that are inherent to both the National Logos in general and its varieties – Bibliologos; secondly, the socio-functional dimension, considering the productive forces, material tools and bibliosphere technologies; thirdly, system-structural measurement, studying signs, symbols and documents that form the system of bibliologos. Hence, there are three private definitions: genetic, functional and system-structural. The generalization of private definitions is the general definition: Bibliologos-Σ is a collective mind of a civilized society in the form of a productive force providing creation, preservation, use of book culture.The idea of the Bibliologos in all interpretations will undoubtedly find use in such areas of modern mental labor as education, science, management for to educate a new type of person. It is obvious. Prospects of the synthesis of book culture and digital culture are problematic. It should be assumed that in the future digital sphere for processing the Big Data arrays, Big Logos will be required as the universal vaccine from ignorance and nonsense.

Fuzzy Relations Matrixes of Damages and Technical Wear Related to Apartment Houses

The research presented in this article was conducted on a representative and purposefully selected sample of 102 residential buildings that were erected in the second half of the nineteenth and early twentieth centuries in the downtown district of Wroclaw (Poland). The degree of the technical wear of an old residential building is determined by the conditions of its maintenance and operation. The diagnosis of the impact of the maintenance of residential buildings on the degree of their technical wear was carried out using quantitative methods in the categories of fuzzy sets and also by using the authors’ own models created in fuzzy conditions. It was proved that the expression of the operational state of a building, considered as the process that plays the greatest role in its accelerated destruction, is mechanical damage to the internal structure of its elements. This damage is determined in the categories of fuzzy sets and has a high frequency and a cumulative effect of occurrence, which are characteristic for buildings in satisfactory and average maintenance conditions. The use of simple operations in fuzzy set calculus enabled the impact of elementary damage that occurs with a specific frequency, as well as the measure of its correlation on the observed technical wear of building elements to be considered. As a result, it was possible to identify the elementary damage that determines the degree of the technical wear of a building element. For each of the selected building elements, the maximal and minimal fuzzy relational equations (damage and technical wear) were determined. Their solutions were given in the form of clear relational matrixes that constitute big data arrays. They define the domain and range of the maximal and minimal fuzzy relations, the height of the fuzzy relations, their differences, and the place of their occurrence between the maximal and minimal dependencies.

Trends and Prospects of the Development of Distributed Computing and Big Data Analytics for Support of Megascience Projects

The creation and development of computer systems for experimental data processing and for data storage and analysis, of search algorithms, and of means for providing access to data are crucial for megascience projects. Information and computing infrastructures necessary for carrying out research tasks of megascience projects are complex distributed heterogeneous systems including systems of extra-massive parallelism and systems of distributed storage of big data arrays.

Digital monitoring of socioeconomic situation of urban population

The subject of this research is the socioeconomic situation of urban population and its response to the processes ongoing within the urban environment. Special attention is turned to digital sociology ensuring the due theoretical-methodological framework and toolset for diagnostic and determination of responses based on formation and processing of big data array extracted from the digital environment represented by social media. Digital environment has potential related in inclusion of new research algorithms and technologies into studying social processes unfolding within urban space. Solution of the task of monitoring of socioeconomic situation of urban population is realized through operationalization of big data using network analysis. The article proposes the procedure for creating in midterm perspective of innovative information system for monitoring the quality of life and socioeconomic situation of urban population based on the comprehensive information maintenance of collection and processing of big data arrays of socioeconomic nature. The author described the components ensuring functionality of the suggested information-analytical system.

Digital identity, digital self-identification, digital profile: problem statement

Problem and goal. Article is devoted to a research of the problematic issues concerning digital identity, digital self-identification of the personality, a digital profile. Not only scientists of the humanitarian direction, but also experts in the field of public administration, finance, edu- cation, philosophy and ethics, hi-tech sectors of economy are engaged in search of answers to these questions, because formation of digital identity is a sphere of personal, public, corporate and national security. The purpose of the research described in article is definition on the basis of the analysis of the developed approaches to digitalization of society of possible problems in the field of formation of digital identity of the personality and identification of the main solutions of such problems. Methodology. The analytical research on identification of the questions characterizing the processes of digitalization of society is conducted. The first block of questions: what is digital identity? What its features? How the digital identity in the conditions of network communication is formed? The second block of questions: how there is a digital self-identification of the personality? What consequences of this process for real and virtual (convergent) life of the individual? The third block of questions: what is a digital profile? Who provider of a digital profile? What do new opportunities and risks of use of digital profiles consist in? Results. During the research it is reasoned that in the conditions of the digital economy assuming universal use of big data arrays, the solution of the problem of control over formation of digital identity of the citizen is of particular importance. Since the earliest age, at the person it is necessary to develop skills of formation of digital identity purposefully. The only way to restore complete control over a digital projection of the person is to make all data on the user open for him. Experience of application of the corresponding digital profiles generates the new risks listed in article. Conclusion. Problems of digital identity, digital identification and a digital profile are subjects for serious public and professional and scientific discussion. The digital identity has more and more increasing impact on human life and society. Formation of digital identity is a question of personal, public and national security. The person needs to learn to form the digital identity.

Phase Composition and Microstructure of UHPHT Vein Glass from Giant Diamondiferous Kara Impact Crater by Raman Mapping

2D and 3D Raman mapping allows to observe “in situ” distribution of phases and relationship of minerals in rock samples, different mineral aggregates, mineral inclusions within crystals and other quantitative spectroscopic studies of mineral substances and materials. The method is realized in accumulating and processing of big data array of Raman spectra (RS) from selected areas of a studied sample with subsequent visualization in XY plane at 2D mapping or in the form of 3D experimental models with XYZ coordinates in a case of 3D Raman mapping. During the mapping different analytical data can be visualized as maps such as spatial distribution of mineral phases, degrees of their crystallinity, orientation of crystallites and their size, residual tension in minerals and inclusions and many other parameters. As 3D Raman mapping demands higher requirements to quality of samples, takes significantly long time expenditure and big data for processing, 2D Raman mapping looks to be the most effective for different mineralogical applications. In this work we demonstrate advantages of 2D Raman mapping on the example of studying of the impact ultrahigh-pressure high-temperature (UHPHT) vein glasses with melt-crystallized ultrahigh-pressure silica phase – coesite, for the first time found within diamondiferous impactites of the giant Kara meteorite crater. The mapping was produced with processing of typical spectra ranges of recognized mineral phases met in analyzed areas. As a result of the conducted studies it has been established what according to spectroscopic data coesite hangs to central parts of UHPHT silica glass located in general matrix of aluminosilicate glass. The sites of silicate glass are characterized by fully amorphous state that is obviously shown in its typical ranges. The areas with a high luminescence correspond to inclusions of smectite micro-drops in UHPHT silica glass isolations that has been confirmed with the accompanying study with use of electron probe microanalysis (EPMA), energy dispersive spectroscopy (EDS) and electron diffraction (ED). The used 2D Raman mapping to the impact vein glasses of the Kara meteorite crater allowed to reveal distribution of mineral phases in UHPHT glass, showing distribution of amorphous and crystalline phases that is undoubted advantage of the Raman spectroscopy. In full text of the paper technical features of Raman 2D mapping are shown that can be useful for similar mineral objects and materials with complex phase structure and different degree of ordering.

The Algorithm for Decision-Making Supporting on the Selection of Processing Means for Big Arrays of Natural Language Data

In this paper decision support algorithm for choosing the processing means of natural language big data arrays is proposed. In the process the algorithm uses the program for evaluating the effectiveness of text analyzers. This program is based on the operation of a fuzzy choice system, which serves to calculate the integral indicator of the text analyzer effectiveness. The quality and efficiency of getting answers to test questions are taken into account when evaluating the effectiveness of text analyzers.

About Scaling of Controlling Information System of Industrial Complex by Streamlining of Big Data Arrays in Compliance with Hierarchy of the Present Lifeworlds

This article discusses the problem of scaling the control information system. Some new type of horizontal scaling of big data array is offered. It consists in structuring of this array in compliance with hierarchy of lifeworlds (Lebenswelt), which become distinguishable in the paradigm of large-scale system. Processes of the "lower" lifeworlds can be so slow that won't be caught by strategic analysis and design instruments of "top" lifeworlds. However, these processes can give some delayed cumulative effect called "black swan effect". The complex formation algorithms recreating the logic of the interconnection of industrial enterprise subsystems are presented. The bases of the choice of temporary horizon extension for large-scale system strategic planning are given.

Convergence of institutes of retail traditional and digital economy

The article gives consideration to the institutes of retail digital economy, basic trends of transformation thereof. It shows a changing role of institutes in trade not only as constraints, but as the catalysts of the consumers’ needs. The differences between traditional retail institutes and electronic commerce institutes have been reviewed, the convergence thereof with the present-day operators along the entire value chain has been demonstrated on the basis of examples of the current retail actors and implementing a fundamentally new strategy of communication between a seller and a buyer.The organization of retail “around buyer” becomes the main convergence driver on the basis of acquisition, analysis and use of big data arrays. The elements of internal and external institutional environment have been presented, except that the identification thereof makes it possible to define the most significant institutes for trading business development as well as the “weakest links” in the system of institutional transformation.

Scheduling algorithms for automatic control systems for technological processes

Wide use of automatic process control systems and the usage of high-performance systems containing a number of computers (processors) give opportunities for creation of high-quality and fast production that increases competitiveness of an enterprise. Exact and fast calculations, control computation, and processing of the big data arrays – all of this requires the high level of productivity and, at the same time, minimum time of data handling and result receiving. In order to reach the best time, it is necessary not only to use computing resources optimally, but also to design and develop the software so that time gain will be maximal. For this purpose task (jobs or operations), scheduling techniques for the multi-machine/multiprocessor systems are applied. Some of basic task scheduling methods for the multi-machine process control systems are considered in this paper, their advantages and disadvantages come to light, and also some usage considerations, in case of the software for automatic process control systems developing, are made.

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.