Frank McQuillan

Frank McQuillan is Director of Product Management at Pivotal, focusing on analytics and machine learning for large data sets. Prior to Pivotal, Frank has worked on projects in the areas of robotics, drones, flight simulation, and advertising technology. He holds a Masters degree from the University of Toronto and a Bachelor's degree from the University of Waterloo, both in Mechanical Engineering.

  • Graph Processing on Greenplum Database using Apache MADlib

    Graph Processing on Greenplum Database using Apache MADlib

    In this post, we address the use of massively parallel processing (MPP) databases like Greenplum Database for graph analytics workloads using Apache MADlib.

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  • Apache MADlib Comes of Age

    Apache MADlib Comes of Age

    MADlib has graduated to a Top Level Project in the Apache Software Foundation (ASF). Here, we describe the journey of MADlib and how it solves real-world problems across a wide variety of industries.

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  • New Tools To Shape Data In Apache MADlib

    New Tools To Shape Data In Apache MADlib

    Apache MADlib includes new utilities that make it easier for data scientists to shape and transform data, and to evaluate the accuracy of predictive models. It is well known that data scientists...

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  • Path Functions in Apache MADlib

    Path Functions in Apache MADlib

    The new release of Apache MADlib (incubating) 1.9 includes several new features, including path functions, which can be used to perform regular pattern matching over a sequence of rows, and then...

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  • MADlib’s Journey To Apache: Math, Stats & Machine Learning Methods

    MADlib’s Journey To Apache: Math, Stats & Machine Learning Methods

    We are excited to announce that our collaborative, open source math, statistics, and machine learning library, known as MADlib, is entering Apache incubation. In addition to hearing from a few...

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  • MADlib 1.7 Release—Adding Generalized Linear Models, Decision Trees, and Random Forest

    MADlib 1.7 Release—Adding Generalized Linear Models, Decision Trees, and Random Forest

    The new release of MADlib 1.7 includes several new features, namely generalized linear models, decision trees, and random forest. These ready-to-use algorithms provide better features,...

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