Titlebook: Computational Science and Its Applications – ICCSA 2020; 20th International C Osvaldo Gervasi,Beniamino Murgante,Yeliz Karaca Conference pr

FLEET

An Analytical Computing Infrastructure for Monitoring Dynamic Networks Based on Knowledge Graphskeholders for improving the network services and performance. First, we analyze problems solved by traditional monitoring systems, and identify the classes of problems such systems cannot solve. Then we propose an analytical monitoring system architecture based on knowledge graphs to address these c

esoteric

纖細

Virtual Testbed: Concept and Applicationsopment are (1) characteristics of mathematical models and their interaction; (2) computational aspects and mapping of algorithms onto hardware; (3) information streams and data smanagement. The authors propose the concept of a virtual private supercomputer as a tool for virtual testbed computer envi

駕駛

Virtual Testbed: Simulation of Air Flow Around Ship Hull and Its Effect on Ship Motionse simulated in the framework of Virtual testbed—a near real-time ship motion simulator. We propose simple model that describes air flow around ship hull with constant initial speed and direction which is based on the law of reflection. On the boundary the model reduces to the known model for potenti

Pathogen

疏遠天際

A Modified Algorithm of Porting a Wave Motioncluster of hybrid architecture. The KPI equation with a source term was numerically modeled to reveal the features of the occurrence of extreme waves. A methodology for the implementation of boundary conditions for the modified KPI equation was also proposed.

inventory

conformity

Evolving Principles of Big Data Virtualizationl one. Storage can be of various types, including portals, archives, showcases, data bases of different varieties, data clouds and networks. They can have synchronous or asynchronous computer connections. Because the type of data is frequently unknown a priori, there is a necessity for a highly flex

毀壞

On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective model with further processing by machine learning methods. The problem of feature selection is discussed in two aspects: selection of the optimal values of time and height when and where the output model data are fixed and selection of fixed set of the most representative cloud parameters (features)

AIL