Bridging the GAP: towards approximate graph analytics

摘要: While there has been a tremendous interest in processing data that an underlying graph structure, existing distributed systems take several minutes or even hours to execute popular algorithms. However, cases, providing approximate answer is good enough. Approximate analytics seeing considerable attention big due its ability produce timely results by trading accuracy, but they do not support analytics. In this paper, we bridge gap and first attempt at realizing We discuss how traditional techniques carry over the usecase. Leveraging characteristics of properties algorithms, propose sparsification technique, machine learning based approach choose apt amount required meet given budget. Our preliminary evaluations show encouraging results.