Database Reference
In-Depth Information
• Commercial Tools:
SAS Enterprise Miner
[17] allows users to run predictive and
descriptive models based on large volumes of data from across the
enterprise. It interoperates with other large data stores, has many
partnerships, and is built for enterprise-level computing and
analytics.
SPSS Modeler
[18] (provided by IBM and now called IBM SPSS
Modeler) offers methods to explore and analyze data through a
GUI.
Matlab
[19] provides a high-level language for performing a
variety of data analytics, algorithms, and data exploration.
Alpine Miner
[11] provides a GUI front end for users to develop
analytic workflows and interact with Big Data tools and platforms
on the back end.
STATISTICA
[20] and
Mathematica
[21] are also popular and
well-regarded data mining and analytics tools.
• Free or Open Source tools:
R and PL/R
[14] R was described earlier in the model planning
phase, and PL/R is a procedural language for PostgreSQL with R.
Using this approach means that R commands can be executed in
database. This technique provides higher performance and is more
scalable than running R in memory.
Octave
[22], a free software programming language for
computational modeling, has some of the functionality of Matlab.
Because it is freely available, Octave is used in major universities
when teaching machine learning.
WEKA
[23] is a free data mining software package with an
analytic workbench. The functions created in WEKA can be
executed within Java code.
Python
is a programming language that provides toolkits for
machine learning and analysis, such as scikit-learn, numpy, scipy,
pandas, and related data visualization using matplotlib.
SQL
in-database implementations, such as
MADlib
[24], provide
an alterative to in-memory desktop analytical tools. MADlib
