Notice of Violation of IEEE Publication Principles: Pipelined-MapReduce: An Improved MapReduce Parallel Programing Model

Notice of Violation of IEEE Publication Principles

"Pipelined-MapReduced: An Improved MapReduce Parallel Programming Model"
by Li Wang, Zhiwei Ni, Yiwen Zhang, Zhang Jun Wu, Liyang Tang
in the Proceedings of the 2011 4th International Conference on Intelligent Computation Technology and Automation, March 2011, pp. 871-874

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. The lead author, Li Wang, was solely responsible for the misconduct, and submitted the paper without the knowledge or consent of the other authors.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"MapReduce Online"
by Tyson Condie, Neil Conway, Peter Alvaro, Joseph M. Hellerstein
in the Proceedings of the 7th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2010), April 2010


MapReduce is a parallel programming model, and used to handle large datasets. The MapReduce program can be automatically concurrent executed in large-scale commodity machines. We proposed an improved MapReduce programming model -- Pipelined-MapReduce, to solve the data intensive of information retrieval problems. Pipelined-MapReduce allows data transfer by pipeline between the operations, expanding the batched MapReduce programming model, and can reduce the completion time, and improve the system utilization rate. The experimental results demonstrate that the implemention of Pipelined-MapReduce can scale well and efficiently process large datasets on commodity machines.