3,427 research outputs found
Early Accurate Results for Advanced Analytics on MapReduce
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.Comment: VLDB201
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
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