Journal of Theoretical & Computational Science

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Journal of Theoretical & Computational Science Theoretical aspects of data science Marcello Trovati Edge Hill University, UK Abstract Data Science is an emerging research field consisting of novel methods and approaches to identify actionable knowledge from the continuous creation of data. The applications of this research field to data-driven sciences has led to the discovery of new research directions, with ground-breaking results. In particular, new mathematical theories have contributed to enhanced Data Science frame- works, focusing on the algorithms and mathematical models to identify, extract and assess actionable information. Furthermore, these theoretical approaches have important implications to decision systems based on Big Data, and their application to a variety of multi-disciplinary scenarios, such as Criminology, eHealth and business analysis. Biography After having obtained his PhD in Mathematics at the University of Exeter in 2007, specializing in theoretical dynamical systems with singularities, he has worked for R&D organizations as well as academic institutions, including IBM Research where he was involved in a number of research projects. In 2016 Marcello joined the Computer Science Department at Edge Hill University as a senior lecturer and he was recently awarded a Readership in Computer Science. He is involved in several research themes and projects. He is co-leading the STEM Data Research centre and is actively involved in the Productivity and Innovation Lab, aiming to collaborate and support SMEs in Lancashire. Marcello’s main interests include: Mathematical Modelling, Data Science, Big Data Analytics, Network Theory, Machine Learning, Data and Text Mining. Publication 1. Big-Data Analytics and Cloud Computing, Theory, Algorithms and Applications, to appear in the Computer Communications and Networks, Springer, 2016. 2. Guide to Security Assurance for Cloud Computing, to appear in the Computer Communications and Networks, Springer, 2016 Global International conference on Big Data Analytics & Data Mining | Singapore | July 21-22, 2020 Citation: Marcello Trovati: Theoretical aspects of data science, Global International conference on Big Data Analytics & Data Mining, Singapore, July21-22,2020, 1 Journal of Theoretical & Computational Science ISSN: 2376-130X Volume 6 | Issue 4.

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A Study on Social Network Analysis Through Data Mining Techniques – a Detailed Survey
ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 IJARCCE International Journal of Advanced Research in Computer and Communication Engineering ICRITCSA M S Ramaiah Institute of Technology, Bangalore Vol. 5, Special Issue 2, October 2016 A Study on Social Network Analysis through Data Mining Techniques – A detailed Survey Annie Syrien1, M. Hanumanthappa2 Research Scholar, Department of Computer Science and Applications, Bangalore University, India1 Professor, Department of Computer Science and Applications, Bangalore University, India2 Abstract: The paper aims to have a detailed study on data collection, data preprocessing and various methods used in developing a useful algorithms or methodologies on social network analysis in social media. The recent trends and advancements in the big data have led many researchers to focus on social media. Web enabled devices is an another important reason for this advancement, electronic media such as tablets, mobile phones, desktops, laptops and notepads enable the users to actively participate in different social networking systems. Many research has also significantly shows the advantages and challenges that social media has posed to the research world. The principal objective of this paper is to provide an overview of social media research carried out in recent years. Keywords: data mining, social media, big data. I. INTRODUCTION Data mining is an important technique in social media in scrutiny and analysis. In any industry data mining recent years as it is used to help in extraction of lot of technique based reports become the base line for making useful information, whereas this information turns to be an decisions and formulating the decisions. This report important asset to both academia and industries.
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