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I'm using ncluster_loader for loading data into Aster express (6.0). But if i want load data from realational
database like MySQL then how should i do it, is there any connector for that?

General format of pivot...
SELECT * FROM pivot(

ON { table_name | view_name | ( query ) }

PARTITION BY col1[, col2, ...]

[ ORDER BY order_by_columns ]

PARTITIONS('col1'[, 'col2', ...])


PIVOT_KEYS('key1', 'key2'[, ...])

PIVOT_COLUMN( 'pivot_column_name' )

I downloaded the aster Express from http://www.asterdata.com/download_aster_express/ but this is version 5.0 which is really old. Is there a newer version somewhere?
Also, the images do not contain the naiveBayesPredict.jar file in the /home/aster/demo/Analytics_Foundation.zip. Any idea on where i can get this?

We will try to parse Apache access log right now and see how badly structured data can be trasfromed into SQL-like table. I suppose you did already install  Aster Developer Express and ready to create your first Aster project.

Teradata Studio Express 14.02 now supports Aster database connectivity. Teradata Studio Express is an information discovery tool for retrieving and displaying data from your Aster database systems. It can be run on multiple operating system platforms, such as Windows, Linux, and Mac OSX.

When learning Aster Data, the first thing we did was logon to Aster Command Terminal (ACT) and start submitting queries.  This is typically done through a SSH client (like Putty).  To customize the ACT interface, I used the Putty options to change the background and foreground colors and change the font and font size.  La

Retailers mine transaction data to track purchasing behavior.  Some of the more popular are Market Basket and Collaborative Filtering

Since taking the Aster Data class, I wanted to learn more about this exciting new technology.  After downloading the 2 VMware images (Queen and Worker), I needed another Worker node to be able to increase the Replication Factor (RF) from 1 to 2.

Naïve Bayes is a set of functions to train a classification model.  A training data set for which we know the outcome (Predictor column) based on input variable columns are used to generate the model. 

We then run the model against a set of input variables for which we do not know the Predictor to see what the model says.  It’s quite similar to a Decision Tree with one big exception; the input data are independent of one other.  This is a strong assumption but it makes the computation of the model extremely simple.

Analytics are evolving to a new world from a focus on transactions to a focus on interactions. The analysis of detailed transactional data provide insight into the value created from customer relationships whereas the analysis of interaction provides insight into the experience provided to a customer.