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The increasing use of information and communication technologies that are based on Big Data and Networks Technologies, in our lives, and the big volumes of data that the companies and governments have to face, have raised multiple challenges to researchers. Our community will have to provide answers to questions like: What are the good solutions to manipulate and analyze large data sets? What are the appropriate mechanisms to transmit these data from one site to another? ...
Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and social science. It involves many domains, such as signal processing, probability models, machine learning, data mining, database, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, etc. High Performance Computing typically deals with smaller, highly structured data sets and huge amount computation. Data Science has emerged to tackle the problem of creating processes and approaches to extracting knowledge or insights from gigantic, unstructured data sets. Networks Communications allow interconnections and exchange of information among various types of nodes ranging from humans, natural sites to computers. Indeed, the heterogeneous character of these ubiquitous connections brings forward the use of complex network technologies and theories.
The International Workshop on Big Data and Networks Technologies (BDNT) will be held at the April 6 - 9, 2020, Warsaw, Poland The aim of BDNT is to bring together researchers, professors and students from around the world to present their latest ideas and research results within the scope of BDNT 2020. The conference will include presentations of contributed papers, poster sessions, and state of the art lectures by invited keynote speakers.
Publication
Example of papers from previous versions of BDNT19: https://www.springer.com/gp/book/9783030236717
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Example of papers from previous versions of BDNT
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