Analysing Extraction Process Influence In Big Data Mining And A Proposed Hybrid Model

During the recent time of big data, a huge volume of unstructured and heterogeneous data is generated from various sources such as audio, video, text and images.In other words, the process of retrieving useful information from big and complex datasets is known as Big Data Mining.In the development of a big data system, the extraction process is an essential aspect as it affects the accuracy, reliability, and efficiency of decision-making.This paper involves the study of multiple data extraction methods and examines how they affect the big data mining.The major part of our research is that we have combine rule-based methods and machine learning (ML) and have created a hybrid approach which will enhance the accuracy and scalability while handling noisy and unstructured data.In addition, discussion the challenges of big data extraction, such as data quality issues, integration across diverse sources, and computational limitations.Optimization strategies are also reviewed.This research highlights an optimized and hybrid extraction strategy which increase data reliability and supports more accurate decision-making in big data infrastructure.