ADAPTIVE BIG DATA COMPRESSION ENGINE USING AI-POWERED HEURISTICS

The Adaptive Big Data Compression Engine powered by AI-driven Heuristics represents a significant advancement in the field of intelligent data processing and storage optimization. As the volume and velocity of digital information continue to grow exponentially traditional compression techniques struggle to deliver performance scalability and adaptability. This project demonstrates that by integrating machine-learning heuristics content-aware classification and high-performance compression algorithms it is possible to overcome these limitations and build a system that is faster smarter and more efficient than conventional solutions. The engine begins with a very simple compression engine and progresses to an engine that is involved not only in aiding the process of decomposition for data but is also involved in determining what type of data is acquired and accordingly which algorithm to use. The feasibility tests for this engine asserted that this engine is but also very viable in the present situation with regards to networking with regards to data transmission with regards to IoT Cloud and Big Data. The final phase of this project also approved this engine. If the above-mentioned technology is integrated with the cost factor optimum of the storage and traffic of data then it will be utilized in the optimum operation of other systems and will enable an organization to perform tasks in a data-intensive fashion. The implication arising from the emergence of the need with the current project to fill in the gap between the requirements of intelligent compression from the raw unstructured big data to the intelligent compression is that a brand-new paradigm with the AI-related data engineering systems has been developed.

1.   Buchanan-Gidumal J. (2022). Impact of Artificial Intelligence in Travel Tourism and Hospitality. Springer Handbooks.  This book highlights real-world applications of AI-driven decision systems. It supports the project’s use of recommendation-style heuristics and adaptive learning models in big data compression.   2.  Goodfellow I. Bengio Y. & Courville A. (2016). Deep Learning. MIT Press.    This text provides machine learning theoretical underpinning’s methods used in the compression engine including neural architectures that guide predictive and adaptive heuristic design.   3.  Sommerville I. (2015). Software Engineering (10th ed.). Pearson.    This book serves as the main reference for the system’s development life-cycle modular architecture documentation standards testing strategy and quality engineering practices used in building the compression engine.   4.  Norman D. (2013). The Design of Everyday Things. Basic Books.    This reference supports the design choices for the system’s interface covering usability user-concentric workflows and intuitive control mechanisms for handling large-scale data operations.   5.  Newman S. (2015). Building Microservices: Designing Fine-Grained Systems. O’ReillyMedia.    This book provides the architectural guidance for designing a scalable micro services-based back-end that supports high-volume data processing and distributed compression workflows.