The Minimal Levels of Abstraction in the History of Modern Computing

From the advent of general-purpose, Turing-complete machines, the relation between operators, programmers, and users with computers can be seen in terms of interconnected informational organisms (inforgs) henceforth analysed with the method of levels of abstraction (LoAs), risen within the Philosophy of Informa- tion (PI). In this paper, the epistemological levellism proposed by L. Floridi in the PI to deal with LoAs will be formalised in constructive terms using category the- ory, so that information itself is treated as structure-preserving functions instead of Cartesian products. The milestones in the history of modern computing are then analysed via constructive levellism to show how the growth of system complexity lead to more and more information hiding

Онтологія аналізу Big Data

The object of this research is the Big Data (BD) analysis processes. One of the most problematic places is the lack of a clear classification of BD analysis methods, the presence of which will greatly facilitate the selection of an optimal and efficient algorithm for analyzing these data depending on their structure.In the course of the study, Data Mining methods, Technologies Tech Mining, MapReduce technology, data visualization, other technologies and analysis techniques were used. This allows to determine their main characteristics and features for constructing a formal analysis model for Big Data. The rules for analyzing Big Data in the form of an ontological knowledge base are developed with the aim of using it to process and analyze any data.A classifier for forming a set of Big Data analysis rules has been obtained. Each BD has a set of parameters and criteria that determine the methods and technologies of analysis. The very purpose of BD, its structure and content determine the techniques and technologies for further analysis. Thanks to the developed ontology of the knowledge base of BD analysis with Protégé 3.4.7 and the set of RABD rules built in them, the process of selecting the methodologies and technologies for further analysis is shortened and the analysis of the selected BD is automated. This is due to the fact that the proposed approach to the analysis of Big Data has a number of features, in particular ontological knowledge base based on modern methods of artificial intelligence.Thanks to this, it is possible to obtain a complete set of Big Data analysis rules. This is possible only if the parameters and criteria of a specific Big Data are analyzed clearly.Исследованы процессы анализа Big Data. Используя разработанную формальную модель и проведенный критический анализ методов и технологий анализа Big Data, построена онтология анализа Big Data. Исследованы методы, модели и инструменты для усовершенствования онтологии аналитики Big Data и эффективной поддержки разработки структурных элементов модели системы поддержки принятия решений по управлению Big Data.Досліджені процеси аналізу Big Data. Використовуючи розроблену формальну модель та проведений критичний аналіз методів і технологій аналізу Big Data, побудовано онтологію аналізу Big Data. Досліджено методи, моделі та інструменти для удосконалення онтології аналітики Big Data та ефективнішої підтримки розроблення структурних елементів моделі системи підтримки прийняття рішень з керування Big Data

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms