Description: Block-Based Analysis is a data processing technique that involves dividing data sets into smaller segments or blocks to facilitate analysis. This methodology allows for more efficient handling of large volumes of information, optimizing both processing time and the ability to detect patterns and anomalies. By segmenting the data, artificial intelligence algorithms can be applied more effectively, as each block can be analyzed individually, allowing for the identification of irregularities that might go unnoticed in a global analysis. This technique is particularly useful in contexts where data is dynamic and generated in real-time, such as in various fields including network monitoring, financial systems, and fraud detection. Additionally, Block-Based Analysis facilitates the parallelization of processes, meaning that multiple blocks can be analyzed simultaneously, increasing system efficiency. In summary, this technique not only improves accuracy in anomaly detection but also optimizes the use of computational resources, becoming an essential tool in the field of artificial intelligence and data analysis.
