Oracle 18c New Oracle Advanced Analytics (Machine Learning) features

    Mar 7, 2018 7:21:00 AM by Brendan Tierney

    With each release of the Oracle Database we get new Machine Learning features, under the umbrella term of Oracle Advanced Analytics option (OAA).

    With Oracle 18c we get the following new features, that include new machine learning algorithms, improvements to machine learning algorithms, and meta-data improvements for registering new R based algorithms.

    These new OAA features include:

    • New Time-Series function : This new function forecasts target value based solely on a known history of target values and uses the popular auto-regressive modelling method.
    • New Model Detail Views : Previously you could inspect the details of a model using a function. This is being phased out and replaced by model view, with the format DM$VA
    • New Neural Networks Algorithm : With the growing interest in deep learning, Oracle have now included a neural network algorithm into the database, thus providing SQL and PL/SQL interfaces to all for easy of use and easy of integration into applications.
    • New Random Forest Algorithm : Random Forests has been proven over the past few years to be very accurate for certain types of classification problems. This algorithm has now been included in the database, with SQL and PL/SQL interfaces.
    • Improved Sampling for Association Rules : A new specialised sampling approach is introduced for Association Rules. This is to improve performance, while maintaining accuracy, for large/big data sets.
    • Algorithm Meta Data Registration : Simplifies the integration of new algorithms in the R extensibility framework. This feature allows a uniform consistent approach of registering new algorithm functions and their settings.
    • New Exponential Smoothing Algorithm : This allows for users to make predictions from time series data, and includes 14 models, including the popular Holt (trend) and Holt-Winters (trend and seasonality) models, and the ability to handle irregular time series intervals.
    • New CUR Decomposition-based Algorithm for Attribute and Row Importance : Most algorithms focus on identifying columns or rows that are important within their data sets. This algorithm has the added feature of also identifying important rows.

    As you can see there are a lot of machine learning new features in Oracle 18c. Each one of these new features will be explored in more detail in separate blog posts.

    Tags: Oracle

    Brendan Tierney

    Written by Brendan Tierney

    Brendan Tierney, Oracle ACE Director, is an independent consultant and lectures on Data Mining and Advanced Databases in the Dublin Institute of Technology in Ireland. He has 22+ years of extensive experience working in the areas of Data Mining, Data Warehousing, Data Architecture and Database Design. Brendan has worked on projects in Ireland, UK, Belgium and USA. Brendan is the editor of the UKOUG Oracle Scene magazine and deputy chair of the OUG Ireland BI SIG. Brendan is a regular speaker at conferences across Europe and the USA and has written technical articles for OTN, Oracle Scene, IOUG SELECT Journal and ODTUG Technical Journal. Brendan has published the following books with Oracle Press book Predictive Analytics using Oracle Data Miner Oracle R Enterprise SQL & PL/SQL from the Experts These books are available on Amazon.