Overview

Technological Advancements in Data Processing for Next Generation Intelligent Systems presents an in-depth exploration of cutting-edge data processing technologies that drive the development of next-generation intelligent systems in the context of the digital transformation era. This comprehensive book delves into the role data plays as a critical asset for organizations across diverse industries, and how recent technological breakthroughs have unlocked unprecedented potential for handling vast data volumes and real-time analysis. The book begins by providing a thorough overview of novel technologies such as artificial intelligence (AI) or machine learning (ML), edge computing, federated learning, quantum computing, and more. These revolutionary technologies, when integrated with big data frameworks, in-memory computing, and AI/ML algorithms, have transformed data processing capabilities, enabling the creation of intelligent systems that fuel innovation, optimize operations, and deliver personalized experiences. The ultimate aim of this integration is to empower devices with the ability to make autonomous intelligent decisions, maximizing computing power. Emphasizing the development of highly efficient next-generation intelligent systems, the book focuses on various architectural approaches for data processing. The emphasis rests on real-time analysis, faster decision-making, enhanced privacy, and efficient processing of large data volumes. Future trends are explored, with an eye on achieving pervasive and fine-grained intelligence through optimized data processing methods for sensing data. This book serves as a valuable resource for research scholars, academicians, and industry professionals working towards the future advancement of optimized intelligent systems and intelligent data processing approaches. The chapters encompass a wide range of topics, including architecture and frameworks for intelligent systems, applications in diverse domains, cloud-based solutions, quantum processing, federated learning, in-memory data processing, real-time stream processing, trustworthy AI for Internet of Things (IoT) sensory data, and more. The integration of blockchain technology for IoT sensory data management and architectural considerations for data processing technologies are extensively discussed, making this an important resource for anyone interested in next generation intelligent systems.

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Shanu Sharma is currently working as an Assistant Professor in Department of Computer Science & Engineering at ABES Engineering College, Ghaziabad (Affiliated to A.P.J Abdul Kalam Technical University, Lucknow). She is having 11.5 years of teaching and research experience. Her research area includes Cognitive computing, Computer Vision, Pattern Recognition and Machine Learning. She has published and presented her work in various National and International Conferences and Journals and currently associated with various reputed International Conferences and journals as Reviewer. She is also serving as a Guest Editor of Special Issue on Intelligent Systems and Application, International Journal of Intelligent Information Technologies (IJIIT), IGI Global and Int. J. of Operations Research and Information Systems (IJORIS), IGI Global. She is a Senior member of IEEE and also an active member of other professional societies like ACM, Soft Computing Research Society and IAENG. Vijayan Sugumaran is Distinguished Professor of Management Information Systems and Chair of the Department of Decision and Information Sciences at Oakland University, Rochester, Michigan, USA. He is also the Co-Director of the Center for Data Science and Big Data Analytics at Oakland University. He received his Ph.D. in Information Technology from George Mason University, Fairfax, Virginia, USA. His research interests are in the areas of Big Data Management and Analytics, Ontologies and Semantic Web, Intelligent Agent and Multi-Agent Systems. He has published over 260 peer-reviewed articles in Journals, Conferences, and Books. He has edited twenty books and serves on the Editorial Board of eight journals. He has published in top-tier journals such as Information Systems Research, ACM Transactions on Database Systems, Communications of the ACM, IEEE Transactions on Big Data, IEEE Transactions on Engineering Management, IEEE Transactions on Education, and IEEE Software . Dr. Sugumaran is the editor-in-chief of the International Journal of Intelligent Information Technologies (IJIIT) . He is the Chair of the Intelligent Agent and Multi-Agent Systems mini-track for Americas Conference on Information Systems (AMCIS 1999–2021). Dr. Sugumaran has served as the Program Chair for the 14th Workshop on E-Business (WeB2015), the International Conference on Applications of Natural Language to Information Systems (NLDB 2008, NLDB 2013, NLDB 2016, and NLDB 2019), 29th Australasian Conference on Information Systems (ACIS 2018), 14th Annual Conference of Midwest Association for Information Systems (MWAIS 2019), 5th IEEE International Conference on Big Data Service and Applications (BDS 2019), and 2022 Midwest Decision Sciences Institute Annual Conference (MWDSI 2022). He also regularly serves as a program committee member for numerous national and international conferences. Website: http://www.sba.oakland.edu/faculty/sugumara

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