Impact Score 4.46
Big Data in Intelligent Information Systems
Big data paradigm is an interdisciplinary field bringing together computer science, mathematics, statistics, and information theory to analyse data for interpretation and prediction. Big data research is characterized by voluminous and incremental datasets and complex data methods. The machine learning methods used in algorithm development are iterative and parallel. These methods can be scaled to handle big data using distributed and parallel computing technologies.
Intelligent and Information Systems provide numerous opportunities for digital transformations including AI, IoT, machine learning, big data, and business intelligence. The amount of data available and its handling has become a centre point for new developments and technologies. Combining AI and machine learning provides organizations with the ability to analyses massive datasets much more reliably and noticeably faster. Big data and its introduction into all areas of business and key industries need a primary way of conducting, and data intelligence provides exactly that. With big data and the volume of information needed to be consumed for different types of investigation and analysis, data intelligence is an extension to the traditional way in which we see and digest data efficiently and extracting the most useful information.
This special issue addresses the issues and challenges posed by several big data problems and give an overview of the state of the art and the future research opportunities. This special issue is opened to submit high quality research contributions from wide range of professions including scholars, researchers, academicians, and Industry people. Original research papers and state of the art reviews will be accepted.
Topics of interest include, but are not limited to, the following scope:
Manuscript submission deadline: 15 July 2021
Notification of acceptance: 15 September 2021
Submission of final revised paper: 15 October 2021
Publication of special issue (tentative): 15 November 2021
Authors should follow the MONET Journal manuscript format described at the journal site. Manuscripts should be submitted on-line through http://www.editorialmanager.com/mone/.
A copy of the manuscript should also be emailed to the Guest Editors at the following email address: [email protected]
Dr. Anandakumar Haldorai, Sri Eshwar College of Engineering, Coimbatore, India.
Email: [email protected] (Handling Editor)
Dr. Sri Devi Ravana, University of Malaya, Malaysia.
Dr. Joan Lu, University of Huddersfield, United Kingdom.
Dr. Arulmurugan Ramu, Presidency University, Bangalore, India.