Paper Info
Title
Early accurate results for advanced analytics on MapReduce
Abstract
Approximate results based on samples often provide the only way in which advanced analytical applications on very massive data sets can satisfy their time and resource constraints. Unfortunately, methods and tools for the computation of accurate early results are currently not supported in MapReduce-oriented systems although these are intended for 'big data'. Therefore, we proposed and implemented a non-parametric extension of Hadoop which allows the incremental computation of early results for arbitrary work-flows, along with reliable on-line estimates of the degree of accuracy achieved so far in the computation. These estimates are based on a technique called bootstrapping that has been widely employed in statistics and can be applied to arbitrary functions and data distributions. In this paper, we describe our Early Accurate Result Library (EARL) for Hadoop that was designed to minimize the changes required to the MapReduce framework. Various tests of EARL of Hadoop are presented to characterize the frequent situations where EARL can provide major speed-ups over the current version of Hadoop.
Year
DOI
Venue
2012
10.14778/2336664.2336675
PVLDB
Keywords
DocType
Volume
arbitrary work-flows,arbitrary function,accurate early result,mapreduce framework,incremental computation,data distribution,advanced analytics,accurate result,early accurate result,massive data set,early result,big data
Journal
5
Issue
ISSN
Citations 
10
Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 10, pp. 1028-1039 (2012)
61
PageRank 
References 
Authors
1.83
17
3
Name
Order
Citations
PageRank
Nikolay Laptev116311.07
Kai Zeng237323.16
Carlo Zaniolo343051447.58