Alexander Koval

By Larysa Globa, Rina Novogrudska, Alexander Koval and Vyacheslav Senchenko

The chapter describes the process of ontology development for different subject domains for application designing. The analysis of existing approaches to ontology development for software platform realization in some subject domains is depicted. The example of ontology model development for telecom operator billing system based on descriptive logic is shown. For ontology model designing, it is proposed to use two formal theories: descriptive logic and set theory, which allow to systematize data and knowledge, to organize search and navigation, and to describe informational and computational recourses according to the meta-notion standards.

Part of the book: Ontology in Information Science

By Larysa Globa, Rina Novogrudska, Alexander Koval and Vyacheslav Senchenko

The proposed research deals with the improvement of engineering knowledge classification and recognition by means of ontology usage. Ontology model allows structure information as well as to raises the effectiveness of search. Research describes the development of ontology models for engineering knowledge in Internet portal and modeling system for the classification and recognition of marine objects. The ontology model usage for the engineering knowledge portal development allows to systematize data and knowledge, to organize search and navigation, to describe informational and computational recourses according to the meta-notion standards. The description of modeling system subject domain is based on ontology that allows to realize the recognition of marine objects based on their parameters.

Part of the book: Ontology in Information Science

By Larysa Globa, Oleksandr Stryzhak, Nataliia Gvozdetska and Volodymyr Prokopets

Global digital transformation requires more productive large-scale distributed systems. Such systems should meet lots of requirements, such as high availability, low latency and reliability. However, new challenges become more and more important nowadays. One of them is energy efficiency of large-scale computing systems. Many service providers prefer to use cheap commodity servers in their distributed infrastructure, which makes the problem of energy efficiency even harder because of hardware inhomogeneity. In this chapter an approach to finding balance between performance and energy efficiency requirements within inhomogeneous distributed computing environment is proposed. The main idea of the proposed approach is to use each node’s individual energy consumption models in order to generate distributed system scaling patterns based on the statistical daily workload and then adjust these patterns to match the current workload while using energy-aware Power Consumption and Performance Balance (PCPB) scheduling algorithm. An approach is tested using Matlab modeling. As a result of applying the proposed approach, large-scale distributed computing systems save energy while maintaining a fairly high level of performance and meeting the requirements of the service-level agreement (SLA).

Part of the book: Scheduling Problems