December 2025
Intermediate to advanced
368 pages
9h 58m
English
Content preview from Distributed and Parallel Computing
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8Paradigm Shifts and Future Directions in Distributed Data Management for Decentralized Networks
Bishnu Kant Shukla1*, Bhupender Parashar2, Vikas Kumar Singla3 and Shivam Verma4
1Department of Civil Engineering, JSS Academy of Technical Education, Noida, India
2Department of Mathematics, JSS Academy of Technical Education, Noida, India
3Department of Mechanical Engineering, JSS Academy of Technical Education, Noida, India
4Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark (New Jersey), USA
Abstract
New data and knowledge management methods are being developed for decentralized systems and the future Internet. The evolution of distributed data management and how it is necessary for decentralized architectures to be more efficient, reliable, and scalable will be examined in this chapter. Novel frameworks and architectures are examined to handle decentralized networks’ massive and varied datasets. Current academic research shows the complexity of configurable data analytics engines and domain-specific languages, which address heterogeneous data sources and high ingestion rates. Advanced data analytics frameworks are needed as the Internet of Things (IoT) becomes more ingrained in decentralized ecosystems. Edge computing designs that reduce network congestion and latency are covered in this chapter. In IoT environments, scalability and real-time processing are crucial. As big data in distributed systems become more important, strong ...
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