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Kyoto Cabinet: a straightforward implementation of DBM Copyright (C) 2009-2012 FAL Labs Last Update: Fri, 04 Mar 2011 23:07:26 -0800 Overview K Means Clustering with Tf-idf Weights Unsupervised learning algorithms in machine learning impose structure on unlabeled datasets. In Prof. Andrew Ng's inaugural ml-class from the pre-Coursera days, the first unsupervised learning algorithm introduced was k-means, which I implemented in Octave for programming exercise 7. Now, after the fact but with a fresh perspective and more experience, I will revisit the k-means algorithm in Java to implement text clustering. Concretely!

Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase comparison (Yes it's a long title, since people kept asking me to write about this and that too :) I do when it has a point.) While SQL databases are insanely useful tools, their monopoly in the last decades is coming to an end. And it's just time: I can't even count the things that were forced into relational databases, but never really fitted them. (That being said, relational databases will always be the best for the stuff that has relations.) But, the differences between NoSQL databases are much bigger than ever was between one SQL database and another.

NodeCellar: Sample Application with Backbone.js, Twitter Bootstrap, Node.js, Express, and MongoDB In my previous post, I shared my recent experience building a RESTful API with Node.js, MongoDB, and Express. In this post, I’m sharing the client application that uses that RESTful API. The Node Cellar application allows you to manage (retrieve, create, update, delete) the wines in a wine cellar database.

You Only Wish MongoDB Wasn't Relational MongoDB = Get Stuff Done Update: Changed blog example to use a normal belongs-to relationship. Update: Added examples of $slice and $elemMatch to show why they don’t work When choosing the stack for our TV guide service, we became interested in NoSQL dbs because we anticipated needing to scale horizontally. We evaluated several and settled on MongoDB. NoSQL "Structured storage" redirects here. For the Microsoft technology also known as structured storage, see COM Structured Storage. A NoSQL (often interpreted as Not Only SQL[1][2]) database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Introduction to Information Retrieval This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. You can order this book at CUP, at your local bookstore or on the internet. The best search term to use is the ISBN: 0521865719.

Hadoop Tutorial home | Cloud Types | Related Technologies What is Hadoop? Miha Ahronovitz, Ahrono & Associates Kuldip Pabla, Ahrono & Associates Hadoop is a fault-tolerant distributed system for data storage which is highly scalable. The scalability is the result of a Self-Healing High Bandwith Clustered Storage , known by the acronym of HDFS (Hadoop Distributed File System) and a specific fault-tolerant Distributed Processing, known as MapReduce. (Hadoop Distributed File System) and a specific fault-tolerant Distributed Processing, known as MapReduce.

Backbone.js and Twitter Bootstrap Sample App Try It Enter a few characters in the Search Box in the upper right corner of the screen, and select an employee. In the Employee view, you can navigate up and down the Org Chart by clicking either the Manager link, or one of the Direct Reports in the sidebar on the right of the screen. MongoDB, Morphia and EmbedMongo » The Cubeia Blog Most of the times when working with online games we run into the need to persist data that is produced by the games. This can be anything from hand history to game state to audit trails for remote calls to other systems. But what they usually have in common is that it is high volume writes, hardly any updates, some reads and that the data has large variation in what we need to store even if it is within the same context. That last part about the data having variations is what makes this interesting.

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