"Dr Alag is a seasoned software professional with over eleven years of commercial software development and management experience and over fifteen years of experience in the machine learning and search areas. His areas of expertise is in building large-scale distributed SaaS applications, applying intelligence and personalization to Web 2.0 applications, SOA and J2EE architectures, delivering high quality software and building high performing engineering teams.

He is currently the VP of Engineering at NextBio, a data search engine in the life sciences areas. Prior to joining NextBio, he worked as a consultant with Johnson and Johnson's BabyCenter where he helped develop their personalization engine. Prior to that he was the Chief Software Architect and Dir. of Application Engineering at Rearden Commerce. He began his career at GE R&D and has held senior technical and management positions at Computing Technologies International and Black Pearl.

Satnam is a Sun Certified Enterprise Architect (SCEA) for the Java platform. His doctoral dissertation from the University of California, Berkeley was in the area of probabilistic reasoning and machine learning. He has a number of peer-reviewed publications, holds one patent and has a few more patents pending in the areas of distributed computing, SOA, travel, web services, and machine learning."

-- http://www.linkedin.com/pub/0/790/487

"The first chapter is free (http://www.manning-source.com/books/alag/alag_meapch1.pdf) and so is the source code used in the book (http://www.manning-source.com/books/alag/CIiA-src.zip).

The book is for Java developers who want to implement “Collective Intelligence” applications in Java. It tells us about extracting and applying data from blogs, wikis and social network applications. People who read this blog know that I am not one to praise, but this book succeeds brilliantly. If you are a Java engineer and work with Web technologies, you must get this book. It covers topics such as computing similarity measures using vector models, Naïve Bayes Classifiers, inverse document frequency (idf), Machine Learning (using the Weka API), building a crawler with regular expressions, collaborative filtering (with links to open source tools), and so on.

Even if you do not work with Java, if you care for high-end Web applications, this book is for you. It reminds me of Lyon’s Java Digital Signal Processing book. It offers the gist of what academia knows, but focuses on what people (engineers and researchers) do in practice.

The book is not meant for academia however. There are references, but no theorem."

-- by Daniel Lemire http://feeds.feedburner.com/~r/daniel-lemire/atom/~3/304207263/