Empowering Data Analytics Using Machine Learning and Data Sharing Through Blockchain Methods

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Abstract
In today’s data-driven world, the convergence of emerging technologies and innovative approaches has revolutionized the landscape of data analytics, paving the way for transformative solutions in decentralized data sharing, real-time communication, and demand forecasting. This paper explores the dynamic synergy of Distributed Ledger Technologies, particularly IOTA Tangle and Directed Acyclic Graph, with the Message Queuing Telemetry Transport protocol and advanced data analytics techniques for predictive insights. The application of IOTA Tangle and DAG in decentralized data sharing is the subject of our investigation. These technologies provide safe, scalable, and impenetrable platforms that support cross-industry collaboration and data analytics. IOTA Tangle and DAG additionally offer tamper-resistant transactions, preserving data credibility and integrity while promoting peer-to-peer data sharing that boosts effectiveness and ownership. The second area of our investigation focuses on applying the MQTT protocol for real-time communication in cross-industry collaborative supply chains. MQTT enables quick decision-making based on real-time data thanks to features like reduced latency, asynchronous communication, and bidirectional capability. Additionally, the strong security characteristics of MQTT improve data integrity and confidentiality, encouraging cooperation and supply chain effectiveness. Our study includes a comparison of different time series models, showing how Deep Matrix Factorization can significantly improve demand forecasting and prediction in collaborative supply chains across different industries. In this paper, we examine the intriguing interaction between IOTA Tangle, DAG, MQTT, and advanced data analytics techniques, ushering in a period of unmatched insights and efficacy in data analytics across various industries.

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