2012/05/31 by Chengjie Qin, Qin, Chengjie, Florin Rusu +1
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Data Stream Mining Techniques #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1206.0051
openalex publication_date 2012/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Online aggregation provides estimates to the final result of a computation during the actual processing. The user can stop the computation as soon as the estimate is accurate enough, typically early in the execution. This allows for the interactive data exploration of the largest datasets. In this paper we introduce the first framework for parallel online aggregation in which the estimation virtually does not incur any overhead on top of the actual execution. We define a generic interface to express any estimation model that abstracts completely the execution details. We design a novel estimator specifically targeted at parallel online aggregation. When executed by the framework over a massive 8TB TPC-H instance, the estimator provides accurate confidence bounds early in the execution even when the cardinality of the final result is seven orders of magnitude smaller than the dataset size and without incurring overhead.