Introduction
Apache Hivemall is a collection of machine learning algorithms and versatile data analytics functions. It provides a number of ease of use machine learning functionalities through the Apache Hive UDF/UDAF/UDTF interface.
Apache Hivemall offers a variety of functionalities: regression, classification, recommendation, anomaly detection, k-nearest neighbor, and feature engineering. It also supports state-of-the-art machine learning algorithms such as Soft Confidence Weighted, Adaptive Regularization of Weight Vectors, Factorization Machines, and AdaDelta.
Architecture
Apache Hivemall is mainly designed to run on Apache Hive but it also supports Apache Pig and Apache Spark for the runtime. Thus, it can be considered as a cross platform library for machine learning; prediction models built by a batch query of Apache Hive can be used on Apache Spark/Pig, and conversely, prediction models build by Apache Spark can be used from Apache Hive/Pig.
