Compressed Data Representation Methods for High-Speed Search

Abstract

The current age of computing revolves around data; the ability to fetch and store large quantities of information has become imperative for systems such as embedded systems and even search engines. Methods of compressed data representation are vital, as they enable faster query execution while reducing the storage space needed. This paper has analyzed such methods. The authors have reviewed bitmap indexing, inverted index compression, succinct data structures, LZ-based schemes, and compressed tries based on the set criteria of practical usefulness, search performance, and space efficiency. Through qualitative metrics, the authors performed a comparative evaluation, which is then represented in a conceptual figure and through tables. Moreover, the paper analyzes potential use case scenarios in domains such as bioinformatics, log management, edge computing, and AI-powered search pipelines. Other issues that have been explored include a balance between compression and query latency, optimizing for heterogeneous hardware, encrypted data search, and searching through encrypted data. The findings illuminate previously unexplored areas of research, including learned indexing, adaptive compression, and searching with minimal energy expenditure.