Data Analytics and Management in Data Intensive Domains

Selected Papers of the XIX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2017)

Moscow, Russia, October 9-13, 2017.

Edited by

Leonid Kalinichenko *
Yannis Manolopoulos **
Nikolay Skvortsov *
Vladimir Sukhomlin ***

* Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, Moscow, Russia
** Aristotle University, Department of Informatics, Thessaloniki, Greece
*** Lomonosov Moscow State University, Moscow, Russia

DAMDID/RCDL conference is formed as a result of transformation of the Russian Conference on Digital Libraries with an intention to create a forum reflecting the urgent challenges of data organization, exploration and analysis in various data intensive domains. The transformation is planned so that the continuity with RCDL should be preserved by the transformed conference as well as the RCDL community formed during the sixteen years of its successful work will also be preserved.

DAMDID/RCDL allows authors to submit their paper either in English or in Russian.
For the papers in Russian the abstracts in English are provided and are placed on the first pages of the papers right after abstracts in Russian.

Table of Contents

Keynote Talk 1

Invited Talk

PhD Workshop

Keynote Talk 2

Data Analysis Projects in Astronomy

Semantic Web Techniques in DID

Special-purpose DID infrastructures 1

Distributed Computing

Special-purpose DID Infrastructures 2

System Efficiency Evaluation

Keynote Talk 3

Data Analysis Projects in Neuroscience

Specific Data Analysis Techniques

Ontological Models and Applications 1

Heterogeneous Database Integration

Text Analysis in Humanities 1

Data Analysis Projects in Various DID

Text analysis in humanities 2

Ontological Models and Applications 2

Organization of Experiments in Data Intensive Research

Digital Library Projects

Knowledge Representation and Discovery

Approaches for Problem Solving in DID

Application of Machine Learning

2017-12-12: submitted by Leonid Kalinichenko, Sergey Stupnikov, metadata incl. bibliographic data published under Creative Commons CC0
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