Data Enhancement Strategy Using Reinforcement Learning And Xgboost With BlockChain Security

The enormous volumes of data generated by Next Generation Sequencing (NGS) technology have transformed genomics and made efficient data analysis techniques necessary. To create data analytics applications, the extraction, transformation, and loading (ETL) process is necessary. In understanding data features, representation, storage, I/O operations, intermediate findings, in-memory analysis, and total processing time, the data format is crucial, as this study emphasizes. Data conformance is required for effective analysis since different machine learning and data mining algorithms require different types and forms of data. In this paper, we explore data format-centric optimization of the ETL process for NGS data analysis. We investigate how computing efficiency and analytical correctness are affected by the choice of data type. By using distributed computing and parallel processing, we evaluate the effectiveness of several data formats in NGS data analysis processes, such as FASTQ, BAM, and VCF. Our study offers suggestions for developing data analytics applications in the genomics field by clarifying the relationship between data format, ETL, and algorithmic needs. We also look at the trade-offs between resource usage and data format choice. In the end, our study contributes to more accurate and efficient genomics research by addressing the urgent need for well-informed data format options in NGS data analysis.