Improved storage of Big Data in DNA using the PCA algorithm

In today’s digital era, we are witnessing an unparalleled surge in data generation, propelled by various sources including social networks, connected devices, online transactions, and IoT (Internet of Things) sensors. This vast amount of data, commonly known as Big Data, brings forth significant opportunities and challenges for businesses and organizations. On one hand, thorough analysis of Big Data offers valuable insights into market trends, streamlining operational processes, and facilitating informed strategic decisions. On the other hand, the effective storage and management of these extensive datasets present considerable challenges due to their sheer volume, diverse formats, and rapid production rate. DNA has emerged as a potentially groundbreaking medium in this context, boasting exceptionally high storage density and durability. Nonetheless, the transformation of digital data to DNA sequences requires a competent management of the data representation and the algorithmic complexity of that task. This article delves into the utilization of the Principal Component Analysis (PCA) algorithm to enhance the storage process of textual data in DNA. Through the application of PCA, we streamline the dimensionality of textual data while preserving its core semantic structure. We validate the efficiency of our approach through comparative analysis of storage performance with and without PCA, underscoring its benefits in terms of storage density, data retrieval, and durability. Our findings propose that integrating the PCA algorithm into the storage process of textual data in DNA could significantly enhance its efficiency and reliability, heralding new advancements in DNA-based data storage methodologies.