Relational Dataset Analytics for Clear Customer Insights


Creating Relational Datasets in HPCC Systems releases a powerful query capability that helped to solve real world questions from our open source community to allow them to make better risk-related business decisions. A part of the dataset was also used to perform ML analytics to determine a real time housing price prediction model. Built-in Visualization Tools also give the customer an easy way to report these insights.

HPCC Systems is a completely free, open source Big Data/Data Lake platform created by LexisNexis Risk Solutions and used by companies globally. The workshop attendee will be provided with interactive code examples and solutions on an actual cluster created for ODSC attendees.

Session Outline:

Here is a sample of some of the real questions that we help to answer in this workshop:
“How many of my customers purchased a Ford Vehicle within 90 days of purchasing a Chevy?”
“I need the aggregate Property values for all properties on “small” streets (CT, LN, WAY, CIR, PL, or
“I need the assessed total value from the most recently reported tax data record for the most recently acquired Property that is not an apartment. If there are multiple properties for the same year, use the one with the highest property value.”

Background Knowledge:

Knowledge of Relational Database Theory. Familiarity with HPCC Systems, ECL, and basic ML concepts is also helpful, but not required.


Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

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