The MADlib Analytics Library or MAD Skills, the SQL

MADlib is a free, open source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that run at scale within a database engine, with no need for data import/export to other tools. The goal is for MADlib to even...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2012-08
Main Authors: Hellerstein, Joe, Ré, Christopher, Schoppmann, Florian, Wang, Daisy Zhe, Fratkin, Eugene, Gorajek, Aleksander, Ng, Kee Siong, Welton, Caleb, Feng, Xixuan, Li, Kun, Kumar, Arun
Format: Article
Language:English
Subjects:
Algorithms
Data base management systems
Data mining
Data transfer (computers)
Libraries
Machine learning
Query languages
Statistical methods
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:MADlib is a free, open source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that run at scale within a database engine, with no need for data import/export to other tools. The goal is for MADlib to eventually serve a role for scalable database systems that is similar to the CRAN library for R: a community repository of statistical methods, this time written with scale and parallelism in mind. In this paper we introduce the MADlib project, including the background that led to its beginnings, and the motivation for its open source nature. We provide an overview of the library's architecture and design patterns, and provide a description of various statistical methods in that context. We include performance and speedup results of a core design pattern from one of those methods over the Greenplum parallel DBMS on a modest-sized test cluster. We then report on two initial efforts at incorporating academic research into MADlib, which is one of the project's goals. MADlib is freely available at http://madlib.net, and the project is open for contributions of both new methods, and ports to additional database platforms.
ISSN:2331-8422