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http://sharlhosting.com/?aa=Off-Prednisone&e54=70 Such graphs can have billions of vertices and millions of nodes (e.g. 100 million movie ratings), and extracting the right information out of them is often a daunting task in terms of computational resources and algorithmic capabilities.
To ease the analysis of such graphs, data is often partitioned across clusters of commodity hardware using big data processing systems such as Hadoop or similar. Hadoop supports a data-parallel computation model, where individual computers work on their own part of the data and results are merged at the end to produce the final solution. However, due to the inherent inter-dependency that characterises graphs, implementing algorithms such as Machine Learning processing algorithms on top of Hadoop becomes difficult and inefficient.
In this regard, Giraph is a new framework for Hadoop which supports a parallel-data computing model of graphs, allowing the machines to exchange messages among them while the algorithm is running. Giraph is now an Open Source project backed by Facebook, which can be seen as an ad-on on top of Hadoop that gives it the capabilities to process graphs at scale.
Over the last three years a team of researchers at Telefonica Digital (http://tid.aerstudio.com/research) has worked on developing advanced Machine Learning algorithms for graph mining that can run on top of Giraph. These research efforts have produced a set of advanced machine learning and ranking algorithms, which have been implemented in a software package call Okapi. Okapi includes state-of-the-art features, such as the latest algorithms for collaborative filtering, recommender systems and social network analysis for Big Data.
Following the spirit of community building and openness that pushed us to contribute to the launch of Firefox OS and foster the Open Web device, we have also decided to release Okapi as an Open Source project under the Apache 2.0 License, hopefully becoming a key open-source Machine Learning library for Apache Giraph. Algorithms include tensor factorization for mean average precision optimisation (TFMAP), collaborative less-is-more filtering (CLiMF), or fake account detection (sybilrank).
More is currently in the pipeline, and Okapi can deal today with Internet scale data, even on a relatively small cluster. The full set of algorithms is currently implemented in http://grafos.ml/index.html#Okapi, and the code has already received multiple community contributions from various academic institutions around the world. We hope an active community of contributors will form to make this effort a great success, developing modern Open Source Big Data tools for a new cloud environments that will benefit all. For more information, please visit http://grafos.ml/