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Special issue on BIG DATA 2022

SPECIAL ISSUE ON “BIG DATA OPTIMIZATION METHODS CHALLENGES, ISSUES AND APPLICATIONS

Description:

There has been a tremendous growth in the amount of data generated in the last two decades. Digitization of process, advent of Internet of Things (IoT), substantial rise of social media usage, adoption of hand-held and wearable devices has become the major cause for incredible rise in data generation. Studies show that in the next five years, the amount of data generated each day is expected to reach around 450 Exabyte’s globally making it viable to create new pathways in understanding social and cultural dynamics. Such data can help the organization / business in decision-making thus providing competitive advantage and sustainability in market. Huge volumes of data generated in variety of forms makes it ‘Big’ hence its challenges and complexity. With the enormous potential of big data, it is important for organizations to know the key tenants of big data optimisation. Big data optimisation is the key to accurate data analytics. If data is not properly optimized, several challenges such as inaccurate findings and delays in processing may arise. However, there are different ways to optimize big data including standardizing format, tuning algorithms, leveraging the latest technology, fixing data errors and removing any latency in processing. Optimized data improves the rate of data processing and the accuracy of results. The complexity and challenges exist in collecting, storing, retrieving, processing, transmission, decision making and applicability in various sectors.

Big data is widely perceived as essential for the accomplishment of the design optimization of complex systems. Much effort has been dedicated to the use of data to enhance the performance of meta-heuristic optimization algorithms for solving large-scale problems in the presence of large amounts of uncertainties. Seamless integration of modern learning and optimization techniques, effective formulation of optimization problem and visualization are few of the challenges in Big Data Optimization. One major hindrance is the massive computational cost required for evaluating the quality of complex engineering systems. The emerging Big Data technologies will eradicate the obstacle to a certain extent by reusing knowledge extracted from huge volumes of high-dimensional, heterogeneous and noisy data. Such knowledge can also be acquired with new visualization techniques. Big data driven optimization will also play a key role in reconstruction of large-scale biological systems.

This Special Issue entitled “Big Data Optimization Methods Challenges, Issues and Applications” provides the necessary background for both academics and practitioners interested to explore optimization algorithms for Big Data and work in the Big Data setting making it beneficial to the society, industry, academia, government and in a variety of industries. This will be useful for the investigators aiming to analyze large scale data and its optimisation methods.

The topics of interest for the special issue include but not limited to the following:

·       Optimization Strategies in Big Data Analytics

·       Convergent parallel algorithms for big data

·       Performance Tools for Big Data Optimization

·       Big Data and Query Optimization Techniques

·       Optimized approaches for storing and accessing files

·       Challenges in Big Data Optimization

·       Optimization Techniques for Learning in Big Data

·       Hadoop Optimization and Performance Tuning techniques

·       Optimization Tools, Techniques and Research Issues for Cloud-Based Big Data Analytics

·       Optimization algorithms for Big Data in Real-time application

·       Machine Learning Optimization Methods and Techniques

 

 

Tentative Timeline:

Submission Deadline of Papers:          20.01.2022

Authors Notification Date:                  24.03.2022

Revised Papers Due Date:                    13.06.2022

Final notification Date:                        05.09.2022

 

Guest Editor Team:

Dr. BalaAnand Muthu [Lead Guest Editor]

Associate Professor,

Department of Computer Science & Engineering,

Adhiyamaan College of Engineering, India

Email: balavdy@gmail.com   

Google Scholar: https://scholar.google.com/citations?hl=en&user=zZOaoVYAAAAJ  

 

BalaAnand Muthu received Ph.D from Anna University, India and working as Associate Professor in the Department of Computer Science & Engineering at Adhiyamaan College of Engineering, India, India. His area of interest includes Big Data Analytics, Social Networks, Internet of Things, Cloud Computing in Healthcare and Environment applications. Served Guest editor in several journals of  IGI, Inderscience, Springer publishers. Also, have published research articles in IEEE Transactions, SCI, SCIE, Scopus indexed peer review journals. Meanwhile, handled Guest lectures, Intensive Workshop, Hands on programming in Hadoop, Spark, Grid & Cloud Computing at various technical institutions around India. He is serving as reviewer in Computer Communication, IEEE Access, Multimedia Tools & Applications, International Journal of Parallel Programming, Enterprise Information System, Computer Networks, Measurement, Computer & Electrical Engineering, Wireless Personal Communication, Cluster Computing, Computational Intelligence, IET Transport Systems and so on. Having membership in IEEE & ACM.

 

 

Dr. Imran Shafique Ansari

James Watt School of Engineering

University of Glasgow,

Official Website: https://www.gla.ac.uk/schools/engineering/staff/imranansari/#researchinterests   

Email: Imran.Ansari@glasgow.ac.uk

Google Scholar: https://scholar.google.com/citations?user=vxTEQZYAAAAJ&hl=en   

 

Imran Shafique Ansari received the B.Sc. degree in Computer Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2009 (with First Honors) and M.Sc. and PhD degrees from King Abdullah University of Science and Technology (KAUST) in 2010 and 2015, respectively. Currently, since August 2018, he is a Lecturer (Assistant Professor) with University of Glasgow, Glasgow, UK. Prior to this, from November 2017 to July 2018, he was a Lecturer (Assistant Professor) with Global College of Engineering and Technology (GCET) (affiliated with University of the West of England (UWE), Bristol, UK). From April 2015 to November 2017, he was a Postdoctoral Research Associate (PRA) with Texas A\&M University at Qatar (TAMUQ). From May 2009 through Aug. 2009, he was a visiting scholar with Michigan State University (MSU), East Lansing, MI, USA, and from Jun. 2010 through Aug. 2010, he was a research intern with Carleton University, Ottawa, ON, Canada.

 

He has been affiliated with IEEE since 2007 and has served in various capacities. He is serving on the IEEE (Global) Nominations and Appointments (N\&A) Committee 2020-2021 and IEEE Communication Society Young Professionals (ComSoc YP) Committee since April 2016. He is part of the IEEE 5G Tech Focus Publications Editorial Board since Feb. 2017. He is an active reviewer for various IEEE Transactions and various other journals. He has served as a TPC for various IEEE conferences. He is a recipient of appreciation for an exemplary reviewer for IEEE Transaction on Communications (TCOM) in 2018 and 2016, a recipient of appreciation for an exemplary reviewer for IEEE Wireless Communications Letters (WCL) in 2017 and 2014, a recipient of post-doctoral research award (PDRA) (first cycle) with Qatar national research foundation (QNRF) in 2014, and a recipient of IEEE Richard E. Merwin student scholarship award in Jul. 2013.

 

Dr. Ansari has authored/co-authored 95+ journal and conference publications. He has co-organized the GRASNET'2016, 2017, 2018 workshops in conjunction with IEEE WCNC'2016, 2017 and IEEE Globecom 2018. His current research interests include free-space optics (FSO), channel modeling/signal propagation issues, relay/multihop communications, physical layer security, full duplex systems, and secure D2D applications for 5G+ systems, among others.

 

 

 

Dr. Xuan Liu (yusuf)

College of Information Engineering (College of Artificial Intelligence),

Yangzhou University, China;

Email: yusuf@yzu.edu.cn  

Personal Website: https://xuanliu.online/  

Google Scholar: https://scholar.google.com/citations?user=N95MHnkAAAAJ&hl=en

 

Xuan Liu (MIEEE’17) received Ph.D. degree in Computer Science and Engineering at Southeast University, China, and is a lecturer currently with School of Information Engineering at Yangzhou University, China. He is also serving as an Advisory Editor of Wiley Engineering Reports, an Associate Editor of Springer Telecommunication Systems, IET Smart Cities, Taylor& Francis International Journal of Computers and Applications and KeAi International Journal of Intelligent Networks, an Area Editor of EAI Endorsed Transactions on Internet of Things, the Lead Guest Editor of Elsevier Internet of Things, Wiley Transactions on Emerging Telecommunications Technologies and Wiley Internet Technology Letters, and the Chair of CollaborateCom 2020 workshop. He served(s) as a TPC Member of ACM MobiCom 2020 workshop, IEEE INFOCOM 2020 workshop, IEEE ICC 2021/2020/2019, IEEE GlobeCom 2020/2019, IEEE WCNC 2021, IFIP/IEEE IM 2021, IEEE PIMRC 2020/2019, IEEE MSN 2020, IEEE VTC 2020/2019/2018, IEEE ICIN2020, IEEE GIIS 2020, IEEE DASC 2019, APNOMS 2020/2019, AdHoc-Now2020, FNC 2020/2019, EAI CollaborateCom 2020/2019, and EAI ChinaCom 2019, etc. Furthermore, he served as a Reviewer for 20+ reputable conferences/journals including IEEE INFOCOM, IEEE ICC, IEEE GlobeCom, IEEE WCNC, IEEE PIMRC, IEEE COMMAG, IEEE TII, IEEE IoT, IEEE CL, Elsevier JNCA, Elsevier FGCS, Springer WINE, Springer TELS, IET SMC, EAI CollaborateCom, and Wiley IJCS, etc. His main research interests focus on UAVs-enabled collaborative networking techniques.