Towards "intelligent compression" in streams

Abstract

With the explosion of information stored world-wide, data intensive computing has emerged as a central area of research. Efficient management and processing of this massively exponential amount of data from diverse sources, such as telecommunication call data records, telescope imagery, online transaction records, web pages, stock markets, medical records (monitoring critical health conditions of patients), climate warning systems, etc., has become a necessity. Removing redundancy from such huge (multi-billion records) datasets results in resource and compute efficiency for downstream processing and constitutes an important area of study. "Intelligent compression" or deduplication in streaming scenarios, for precise identification and elimination of duplicates from the unbounded data stream is a greater challenge given the real-time nature of data arrival. Stable Bloom Filters (SBF) [13] address this problem to a certain extent. However, SBF suffers from a high false negative rate and slow convergence rate, thereby rendering it inefficient for applications with low false negative rate tolerance.