Zaharia, M., Konwinski, A. and Joseph, A.D. (2008) Improving MapReduce Performance in Heterogeneous Environment. Proceedings of the 8th USENIX Conference on Operating Systems De-sign and Implementation, San Diego, 8-10 December 2008, 9-15.

has been cited by the following article:

• TITLE: The Optimization and Improvement of MapReduce in Web Data Mining

  AUTHORS: Jun Qu, Chang-Qing Yin, Shangwei Song

  KEYWORDS: Cloud Computing, Web Data, MapReduce, Map-Reduce-Merge

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.8 No.8, August 24, 2015

  ABSTRACT: Extracting and mining social networks information from massive Web data is of both theoretical and practical significance. However, one of definite features of this task was a large scale data processing, which remained to be a great challenge that would be addressed. MapReduce is a kind of distributed programming model. Just through the implementation of map and reduce those two functions, the distributed tasks can work well. Nevertheless, this model does not directly support heterogeneous datasets processing, while heterogeneous datasets are common in Web. This article proposes a new framework which improves original MapReduce framework into a new one called Map-Reduce-Merge. It adds merge phase that can efficiently solve the problems of heterogeneous data processing. At the same time, some works of optimization and improvement are done based on the features of Web data.