Dear Colleagues,

Diverse multidisciplinary approaches are being continuously developed and advanced to address the challenges that Big Data research raises. In particular, the current academic and professional environments are working to produce algorithms, theoretical advances in big data science, to enable the full utilisation of its potential, and better applications. This Special Issue focuses on the dissemination of original contributions to discuss and explore theoretical concepts, principles, tools, techniques, and deployment models in the context of Big Data. Via the contribution of both academics and industry practitioners, the current approaches for the acquisition, interpretation, and assessment of relevant information will be addressed to advance the state-of-the-art Big Data technology.

This Special Issue aims to collect state-of-the-art breakthroughs, including but not limited to the following topics:

• Statistical and dynamical properties of Big Data;
• Applications of machine learning for information extraction;
• Hadoop and Big Data;
• Data and text mining techniques for Big Data;
• Novel algorithms in classification, regression, clustering, and analysis;
• Distributed systems and cloud computing for Big Data;
• Big Data applications;
• Big Textual/Natural Language Data;
• Theory, applications and mining of networks associated with Big Data;
• Large-scale network data analysis;
• Data reduction, feature selection, and transformation algorithms;
• Data visualisation;
• Distributed data analysis platforms;
• Scalable solutions for pattern recognition;
• Stream and real-time processing of Big Data;
• Information quality within Big Data;
• Threat detection in Big Data.

Prof. Marcello Trovati
Prof. Yannis Korkontzelos
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sci is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Big Data
• Data science
• Mathematical modelling
• Text mining
• Natural language processing
• AI
• Data mining