Big Data Management practical optimization and implementation of algorithms for the 21 century data evolution (near real time) data processing for the data intensive application

Sokolovski, Aleksandar and Gelev, Saso (2016) Big Data Management practical optimization and implementation of algorithms for the 21 century data evolution (near real time) data processing for the data intensive application. In: Четврта меѓународна научна конференција, „Науката - подршка на развојот во Југоисточна Европа“, 23-24 Dec 2016, Skopje, Macedonia.

[img]
Preview
Text
Aleksandar Saso 2 trud.pdf

Download (1776Kb) | Preview

Abstract

The scope of this research paper is one very important aspects nowadays, the security and management of one big data, the data in today information bases world play mayor roll in all aspect of business. In this paper, a data evolution model of Virtual DataSpace (VDS) is proposed for managing the big data lifecycle. Firstly, the concept of data evolution cycle is defined, and the lifecycle process of big data management is described. Based on these, the data evolution lifecycle is analyzed from the data relationship, the user requirements, and the operation behavior. Secondly, the classification and key concepts about the data evolution process are described in detail. According to this, the data evolution model is constructed by defining the related concepts and analyzing the data association in VDS, for the capture and tracking of dynamic data in the data evolution cycle. Then we discuss the cost problem about data dissemination and change. Finally, as the application case, the service process of dynamic data in the field of materials science is described and analyzed. We verify the validity of data evolution modeling in VDS by the comparison of traditional database, dataspace, and VDS. It shows that this analysis method is efficient for the data evolution processing, and very suitable for the data-intensive application and the real-time dynamic service. Keywords: Big Data; Lifecycle; Virtual DataSpace (VDS); Data Evolution

Item Type: Conference or Workshop Item (Paper)
Subjects: Engineering and Technology > Electrical engineering, electronic engineering, information engineering
Divisions: Faculty of Electrical Engineering
Depositing User: Saso Gelev
Date Deposited: 19 Dec 2017 10:01
Last Modified: 19 Dec 2017 10:01
URI: http://eprints.ugd.edu.mk/id/eprint/18759

Actions (login required)

View Item View Item