Recent Advances in Big Data Analytics, Internet of Things and Machine Learning

Big data analytics is a rapidly expanding research area spanning the fields of computer science, information management, and has become a ubiquitous term in understanding and solving complex problems in different disciplinary fields such as engineering, applied mathematics, medicine, computational biology, healthcare, social networks, finance, business, government, education, transportation and telecommunications. The utility of big data is found largely in the area of Internet of Things (IoT). Big data is used to build IoT architectures which include things-centric, data-centric, service-centric architecture, cloud-based IoT. Technologies enabling IoT include sensors, radio frequency identification, low power and energy harvesting, sensor networks and IoT services mainly include semantic service management, security and privacy-preserving protocols, design examples of smart services. To effectively synthesize big data and communicate among devices using IoT, machine learning techniques are employed. Machine learning extracts meaning from big data using various techniques which include regression analysis, clustering, bayesian methods, decision trees and random forests, support vector machines, reinforcement learning, ensemble learning and deep learning.

This special issue is intended to report high-quality research on recent advances toward big data analytics, internet of things and machine learning, more specifically to the state-of-the-art approaches, methodologies and systems for the design, development, deployment and innovative use of machine learning techniques on big data and to communicate among various embedded devices using IoT. Authors are solicited to submit complete unpublished papers in the following topics. Topic includes but not restricted to:

• Relational and Non-relational big data stores
• Querying big data and Managing clusters of big data
• Feeding data to cluster and Analyzing streams of big data
• Cognitive computing for Ecommerce and Security
• IoT system architecture and Enabling technologies
• Communication and Networking protocols for IoT
• IoT services and Applications optimizing the use of big data
• Methods of processing health care data over IoT-Cloud
• Medical IoT and Predictive Models
• Regression, Classification and Clustering for big data analysis
• Associative rule learning and Reinforcement learning
• Deep learning and Artificial neural network for optimizing big data

Submission Guidelines

Original, high-quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences are sought. Papers will be peer-reviewed by independent reviewers and selected based on originality, scientific quality and relevance to this Special Issue. The journal editors will make final decisions about the acceptance of the papers. Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the Future Generation Computer Systems at http://ees.elsevier.com/fgcs/. Authors should select SI “Data Analytics” when they reach the “Issue” step in the submission process.

Important dates

• Paper submission due: September 30, 2017
• First-round acceptance notification: February 28, 2018
• Revision submission: April 15, 2018
• Notification of final decision: July 30, 2018
• Submission of final paper: August 30, 2018
• Publication date: November 30, 2018

Guest editors

Roshan Joy Martis (Leading Guest Editor)
Vivekananda College of Engineering & Technology, India
[email protected]

Hong Lin
University of Houston Downtown, USA
[email protected]

Varadraj Prabhu Gurupur
University of Central Florida, USA
[email protected]

Aminul Islam
University of Louisiana at Lafayette, USA
[email protected]

Steven Lawrence Fernandes
Sahyadri College of Engineering & Management, India
[email protected]