Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
ClusterViz is a software to visualize the clustering process using the family of k-means algorithms. The program is free software under the GNU General Public License (GPL). ClusterViz allows to cluster data while visualizing an up to three dimensional projection. The clustering process is visualized using OpenGL. As clustering algorithms the family of k-means algorithms is implemented, including mixture models.
I posted an updated tech demo of RhNav - Rhizome Navigation visualizing user behavior of this blog. The graph is now centered around the page where most time is spent. Noise created by search engine robots is filtered which should clear things up quite a
The Boost Graph Library Python bindings (which we refer to as "BGL-Python") expose the functionality of the Boost Graph Library and Parallel Boost Graph Library as a Python package, allowing one to perform computation-intensive tasks on graphs (or network
D. Satsangi, K. Srivastava, and Gursaran. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2 (2):
129-149(April 2012)
S. Shankar, G. Rajendra, K. Ashok, and G. Nanaso. International Journal on Recent and Innovation Trends in Computing and Communication, 3 (3):
1361--1366(March 2015)
A. Papadopoulos, and Y. Manolopoulos. Database and Expert Systems Applications, 1999. Proceedings. Tenth International Workshop on, page 174-178. (1999)