Parallel Computing to Predict Breast Cancer Recurrence on SEER Dataset using Map-Reduce Approach

Parallel Computing to Predict Breast Cancer Recurrence on SEER Dataset using Map-Reduce Approach

Due to the late overpowering development rate of large scale data, the advancement of handling faster processing algorithms with optimal execution has turned into a critical need of the time. In this paper, parallel Map-Reduce algorithm is proposed, that encourages concurrent participation of various computing hubs to develop a classifier on SEER breast cancer data set. Our algorithm can prompt...

Teaching Map-reduce Parallel Computing in CS1

Cluster-based map-reduce programming frameworks such as Apache Hadoop make compelling pedagogical examples of parallel computing in undergraduate CS education. Although the basic notions of map-reduce computation are at an appropriate level for introductory CS students, directly programming Hadoop is too difficult for most beginning students. However, the strategically simplified WebMapReduce s...

Complexity Measures for Map-Reduce, and Comparison to Parallel Computing

The programming paradigm Map-Reduce [3] and its main open-source implementation, Hadoop [1], have had an enormous impact on large scale data processing. Our goal in this expository writeup is twofold: first, we want to present some complexity measures that allow us to talk about Map-Reduce algorithms formally, and second, we want to point out why this model is actually different from other mode...

Using Parallel Computing to Reduce CPU Power

Generalized Approach for Data Anonymization Using Map Reduce on Cloud

Data anonymization has been extensively studied and widely adopted method for privacy preserving in data publishing and sharing scenario. Data anonymization is hiding up of sensitive data for owner’s data record to avoid unidentified Risk. The privacy of an individual can be effectively preserved while some aggregate information is shared to data user for data analysis and data mining. The prop...