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Comparing Map-Reduce and FREERIDE for Data-Intensive Applications
 

Summary: Comparing Map-Reduce and FREERIDE for
Data-Intensive Applications
Wei Jiang Vignesh T. Ravi Gagan Agrawal
Department of Computer Science and Engineering
The Ohio State University Columbus OH 43210
{jiangwei,raviv,agrawal}@cse.ohio-state.edu
Abstract--Map-reduce has been a topic of much interest in
the last 2-3 years. While it is well accepted that the map-reduce
APIs enable significantly easier programming, the performance
aspects of the use of map-reduce are less well understood. This
paper focuses on comparing the map-reduce paradigm with a
system that was developed earlier at Ohio State, FREERIDE
(FRamework for Rapid Implementation of Datamining Engines).
The API and the functionality offered by FREERIDE has many
similarities with the map-reduce API. However, there are some
differences in the API. Moreover, while FREERIDE was moti-
vated by data mining computations, map-reduce was motivated
by searching, sorting, and related applications in a data-center.
We compare the programming APIs and performance of the
Hadoop implementation of map-reduce with FREERIDE. For

  

Source: Agrawal, Gagan - Department of Computer Science and Engineering, Ohio State University

 

Collections: Computer Technologies and Information Sciences