Analytical platform for socio-economic studies

Jul 9, 2021, 11:45 AM
15m
407 or Online - https://jinr.webex.com/jinr/j.php?MTID=m573f9b30a298aa1fc397fb1a64a0fb4b

407 or Online - https://jinr.webex.com/jinr/j.php?MTID=m573f9b30a298aa1fc397fb1a64a0fb4b

https://jinr.webex.com/jinr/j.php?MTID=m573f9b30a298aa1fc397fb1a64a0fb4b
Sectional reports 9. Big data Analytics and Machine learning Big data Analytics and Machine learning.

Speaker

Sergey Belov (Joint Institute for Nuclear Research)

Description

Started in natural sciences, the high demand for analyzing a vast amount of complex data reached such research areas as economics and social sciences. Big Data methods and technologies provide new efficient tools for researches. In this paper, we discuss the main principles and architecture of the digital analytical platform aimed to support socio-economic applications. Integrating specific open-source solutions, the platform intended to cover full-cycle data analysis and machine learning experiments, from data gathering to visualization. One of the system's primary goals is to deliver the advantage of the cloud and distributed computing and GPU accelerators with Big Data analysis techniques. The authors present the approach of building the platform from low-level services such as storage, virtual infrastructure, pass-through authentication, up to data flows processing, analysis experiments, and results representation.

Primary authors

Sergey Belov (Joint Institute for Nuclear Research) Anna Ilina (Joint Institute for Nuclear Research) Javad Javadzade (JINR) Ivan Kadochnikov (JINR) Vladimir Korenkov (JINR) Igor Pelevanyuk (Joint Institute for Nuclear Research) Roman Semenov (JINR) Petr Zrelov (LIT JINR)

Presentation materials

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