160,920 research outputs found
BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking
Data generation is a key issue in big data benchmarking that aims to generate
application-specific data sets to meet the 4V requirements of big data.
Specifically, big data generators need to generate scalable data (Volume) of
different types (Variety) under controllable generation rates (Velocity) while
keeping the important characteristics of raw data (Veracity). This gives rise
to various new challenges about how we design generators efficiently and
successfully. To date, most existing techniques can only generate limited types
of data and support specific big data systems such as Hadoop. Hence we develop
a tool, called Big Data Generator Suite (BDGS), to efficiently generate
scalable big data while employing data models derived from real data to
preserve data veracity. The effectiveness of BDGS is demonstrated by developing
six data generators covering three representative data types (structured,
semi-structured and unstructured) and three data sources (text, graph, and
table data)
SInC: An accurate and fast error-model based simulator for SNPs, Indels and CNVs coupled with a read generator for short-read sequence data
We report SInC (SNV, Indel and CNV) simulator and read generator, an
open-source tool capable of simulating biological variants taking into account
a platform-specific error model. SInC is capable of simulating and generating
single- and paired-end reads with user-defined insert size with high efficiency
compared to the other existing tools. SInC, due to its multi-threaded
capability during read generation, has a low time footprint. SInC is currently
optimised to work in limited infrastructure setup and can efficiently exploit
the commonly used quad-core desktop architecture to simulate short sequence
reads with deep coverage for large genomes. Sinc can be downloaded from
https://sourceforge.net/projects/sincsimulator/
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