A New Way of Accelerating Web by Compressing Data with Back Reference-Prefer Geflochtener

This research focused on the synthesis of an iterative approach to improve speed of the web and also learning the new methodology to compress the large data with enhanced backward reference preference. In addition, observations on the outcomes obtained from experimentation, group-benchmarks compressions, and time splays for transmissions, the proposed system have been analysed. This resulted in improving the compression of textual data in the Web pages and with this it also gains an interest in hardening the cryptanalysis of the data by maximum reducing the redundancies. This removes unnecessary redundancies with 70% efficiency and compress pages with the 23.75- 35% compression ratio.