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Get going with a minimum of fuss. 

• Installation Guide
• Quick Start Guide for Users
• Quick Start Guide for Developers

 Developer Documentation

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Contribute to the project.

• Contribution Guidelines
• Documentation Guide (Doxygen)
• Ideas for contribution 

 General Information

Learn about MADlib.

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See how the pieces fit together. 

• Architecture
• Module Anatomy 

 Third Party Components

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MADlib incorporates material from the following third-party components:

 

1. argparse 1.2.1 "provides an easy, declarative interface for creating command line tools"
2. Boost 1.47.0 (or newer) "provides peer-reviewed portable C++ source libraries"
3. doxypy 0.4.2 "is an input filter for Doxygen"
4. Eigen 3.2.2 "is a C++ template library for linear algebra"
5. PyYAML 3.10 "is a YAML parser and emitter for Python"
6. PyXB 1.2.4 "is a Python library for XML Schema Bindings"

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 Licensing

License information regarding MADlib and included third-party libraries can be found inside thethe license directory.

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 Release Notes

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Historical release notes for releases prior to move to ASF. 

 Papers and Talks

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• MAD Skills : New Analysis Practices for Big Data (VLDB 2009)
• Hybrid In-Database Inference for Declarative Information Extraction (SIGMOD 2011)
• Towards a Unified Architecture for In-Database Analytics (SIGMOD 2012)
• The MADlib Analytics Library or MAD Skills, the SQL (VLDB 2012)

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 Related Software

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• PivotalR

   

  -

  PivotalR also

  lets the user run the functions of the open-source big-data machine learning

  package 

  package MADlib

   directly

  directly from R.

• PyMADlib  - PyMADlib is a python nascent Python wrapper for MADlib, which brings you the power and flexibility of python with the number crunching power of of MADlib.