A Modified Approach to Big Data Compression with ArrayBuffer and Huffman Algorithm

Data compression plays a vital role in optimizing both storage utilization and transmission efficiency in modern digital systems. This research introduces a novel compression algorithm, termed ABHCM, which integrates the ArrayBuffer handling mechanism with the Huffman coding framework. By using the ArrayBuffer technique it is possible to directly manipulate binary datasets, helping them to convert into compact forms while reducing both memory requirements and processing time. Huffman coding is a widely used lossless compression method that preserves the original data throughout the entire compression and decompression process. When these two techniques are combined, they offer improved control over binary streams and are especially useful when handling large datasets or streaming data in real time. In experimental testing, the proposed ABHCM algorithm achieved a compression rate of around 87%, which highlights its potential for use in various digital applications.