A Novel Big Data Storage Reduction Model for Drill Down Search

摘要

Multi-level searching is called Drill down search. Right now, no drilldown search feature is available in the existing search engines like Google,Yahoo, Bing and Baidu. Drill down search is very much useful for the end userto find the exact search results among the huge paginated search results. Higherlevel of drill down search with category based search feature leads to get the mostaccurate search results but it increases the number and size of the file system. Thepurpose of this manuscript is to implement a big data storage reduction binary filesystem model for category based drill down search engine that offers fast multilevelfiltering capability. The basic methodology of the proposed model stores thesearch engine data in the binary file system model. To verify the effectiveness ofthe proposed file system model, 5 million unique keyword data are stored into abinary file, thereby analysing the proposed file system with efficiency. Someexperimental results are also provided based on real data that show our storagemodel speed and superiority. Experiments demonstrated that our file systemexpansion ratio is constant and it reduces the disk storage space up to 30% withconventional database/file system and it also increases the search performance forany levels of search. To discuss deeply, the paper starts with the short introductionof drill down search followed by the discussion of important technologies used toimplement big data storage reduction system in detail.