Tuesday, December 30, 2014

Scoring data with ADAPA using Pentaho Data Integration

Predictive model integration for MySQL, Microsoft SQL Server, Oracle and PostgreSQL


The main use of predictive models is to generate predictions for new data. This data frequently resides in databases like MySQL, and the ADAPA scoring engine needs a way to easily access it. One way of accomplishing this is by using the Pentaho Data Integration (PDI) tool, and in this post we outline how to score data from relational databases using the ADAPA REST API and PDI.

PDI provides an easy to use point-and-click interface to manage the whole workflow: retrieving the data, scoring it through ADAPA, and saving the results elsewhere. It is possible to use PDI to read and write to different databases, including MySQL, Microsoft SQL Server, Oracle, PostgreSQL, and others. PDI can also act as a client to the ADAPA Scoring Engine by leveraging the ADAPA REST API, and take care of transforming the data into necessary formats - JSON and URL in this case.

Prior to starting, we assume that:
  • PDI is installed
  • Data to be scored is stored in either MySQL, Microsoft SQL Server, Oracle or PostgreSQL
  • A PMML model for the data is deployed and available through the ADAPA REST API.

The process is built and executed in PDI. The transformation should consist of the following steps:
  • Retrieve data from the database
  • Transform to a JSON object
  • Convert the JSON object to a URL as a method to transmit it
  • Send URL to ADAPA through REST API
  • Capture ADAPA output
  • Write the scoring result back to a flat file

For detailed step-by-step instructions using a neural network model deployed in ADAPA, please review the following videos:


Monday, December 29, 2014

Using MySQL as a Client to the ADAPA Scoring Engine

Predictive analytics scoring with MySQL and ADAPA


In this blog post, we outline how to use a MySQL database as a client to the ADAPA Scoring Engine by leveraging the ADAPA REST API to execute a predictive analytics model based on the Predictive Model Markup Language (PMML) industry standard.

We assume that:
  • MySQL and cURL are installed
  • Necessary MySQL tables are already created
  • A PMML model for the data is deployed and available through the ADAPA
  • REST API.
One option to make API calls from MySQL is by using the MySQL-UDF-HTTP package, which enables creation of user defined functions for HTTP REST Operations in a database. This package is available on Google Code and will be installed on top of MySQL. We can leverage the User Defined Functions (UDFs) created with this package to make REST API calls to ADAPA from MySQL. Specifically, we use HTTP GET requests to the ADAPA engine to score one record at a time. An advantage of using these functions is that we can easily write the scores back to the database.

In addition, the scoring process can be automated with database triggers. Triggers automatically execute database queries when specified events occur. In this case, we can write functions to score and update or insert records, and set triggers to execute these functions on update and insert events. The HTTP UDF is called by the scoring function to send a GET request to the ADAPA REST API.

Simply using SQL and UDFs, the above enables us to easily execute complex predictive analytics models directly from one of the most commonly used databases, score the records, and write the results back into a database table.

A step-by-step tutorial, including installing MySQL-UDF-HTTP and writing functions and triggers, is available in this video.

Tuesday, December 23, 2014

451 Research Initiates Coverage of Zementis

2014 has been a busy year of growth for Zementis, as our customers, business partners and employees have noticed. Industry analysts have also noticed, with two leading research firms initiating coverage of Zementis in 2014. In May, Gartner included Zementis on its short list of “Cool Vendors in Data Science”, and just this week, 451 Research published its first report on Zementis.

451 Research is well known in the technology sector as a highly respected research and advisory company, and is especially noted for its coverage of emerging technologies and the companies that bring these technologies to market. Within the Enterprise Software sector, Krishna Roy leads 451’s analytical efforts in the realm of business applications and software infrastructure, including big data analytics and predictive analytics. She has a longstanding background in technology journalism, and covers the big data analytics segment extensively. 451’s subscribers will find an amazing number of insightful reports on big data and predictive analytics that bear her byline.

The universe of companies in her analytical portfolio numbers more than 100, affording her a fantastic vantage point from which to study the technologies and market dynamics that shape the competitive landscape and define the market’s evolutionary trajectory. This breadth of coverage also makes her schedule extremely full. Zementis is honored that she has chosen to devote some of her scarce time to studying our company and imparting her perspectives and insights.

To access the report, click on the link below:

Impact Report - December 22, 2014







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