Aho-Corasick Trees for Efficient Data Storage and Machine Learning

Abstract

In this work we present to reader the novel research on account for efficiency of compression algorithms like Lempel-Ziv Welch and Aho-Corasick trees We use them to build the proper storage which is called file system in a separate or generalized stream of data These streams weren t adopted before for big data to be compressed and queried at a fast pace We will show further that this is the most efficient model for storing arrays of data on a server end for a final file system The efficient algorithm for Machine Learning on Aho-Corasick trees is also presented which performs the query in linear time without getting more time on the models like neural networks which are very hardware demanding nowadays The data structure like trie by Turing Award winner Alfred V Aho and Margaret J Corasick remain of big potential in the present time and are subjected to extensive research in this work Keywords trie compression storage machine learning