Paper Info
Title
Big data text-oriented benchmark creation for Hadoop
Abstract
Massive-scale Big Data analytics is representative of a new class of workloads that justifies a rethinking of how computing systems should be optimized. This paper addresses the need for a set of benchmarks that system designers can use to measure the quality of their designs and that customers can use to evaluate competing systems offerings with respect to commonly performed text-oriented workflows in Hadoop™. Additions are needed to existing benchmarks such as HiBench in terms of both scale and relevance. We describe a methodology for creating a petascale data-size text-oriented benchmark that includes representative Big Data workflows and can be used to test total system performance, with demands balanced across storage, network, and computation. Creating such a benchmark requires meeting unique challenges associated with the data size and its often unstructured nature. To be useful, the benchmark also needs to be sufficiently generic to be accepted by the community at large. Here, we focus on a text-oriented Hadoop workflow that consists of three common tasks: categorizing text documents, identifying significant documents within each category, and analyzing significant documents for new topic creation.
Year
DOI
Venue
2013
10.1147/JRD.2013.2240732
IBM Journal of Research and Development
Keywords
Field
DocType
text-oriented workflows,benchmark creation,petascale data-size text-oriented benchmark,significant document,new class,computing system,representative big data workflows,new topic creation,big data,system designer,massive-scale big data analytics,text-oriented hadoop workflow
Data science,Computer science,Petascale computing,Big data,Workflow,Computing systems,Computation
Journal
Volume
Issue
ISSN
57
3-4
0018-8646
Citations 
PageRank 
References 
15
1.26
4
Authors
5
Name
Order
Citations
PageRank
Gattiker, A.1151.26
F. H. Gebara2181.89
H. P. Hofstee350754.92
J. D. Hayes4151.26
A. Hylick5151.26