Embedding Transformation Data Pipeline into ML Model using Oracle Data Mining

I’ve written several blog posts about how to use the DBMS_DATA_MINING.TRANSFORM function to create various data transformations and how to apply these to your data. All of these steps can be simple enough to following and re-run in a lab environment. But the real value with data science and machine learning comes when you deploy the models into production and have the ML models scoring data as it is being produced, and your applications (more...)

Importance of setting Fetched Rows size for Database Query using Golang

When issuing queries to the database one of the challenges every developer faces is how to get the results quickly. If your queries are only returning a small number of records, eg. < 5, then you don’t really have to worry about execution time. That is unless your query is performing some complex processing, joining lots of tables, etc.

Most of the time developers are working with one or a small number of records, using (more...)

Rittman Mead at Kscope 2019

Rittman Mead at Kscope 2019

June is time for one of my favourite conferences: Kscope! This year the location is Seattle and the Agenda is impressive. The event starts on Sunday with the Symposiums driven by Oracle Product Managers and divided by stream of interest.

The main conference is Monday-Wednesday with the Thursday dedicated to Deep Dive sessions. On Tuesday morning the Analytics track attention will be shifted to Skywalker Ranch for live-stream of the Oracle Analytics Summit!

I'll be (more...)

Transforming Outliers in Oracle Data Mining

In previous posts I’ve shown how to use the DBMS_DATA_MINING.TRANSFORM function to transform data is various ways including, normalization and missing data. In this post I’ll build upon these to show how to outliers can be handled.

The following example will show you how you can transform data to identify outliers and transform them. In the example, Winsorsizing transformation is performed where the outlier values are replaced by the nearest value that is not (more...)

Examples of Machine Learning with Facial Recognition

In a previous blog post I gave some examples of how facial images recognition and videos are being used in our daily lives. In this post I want to extend this with some additional examples. There are ethical issues around this and in some of these examples their usage has stopped. What is also interesting is the reaction on various social media channels about this. People don’t like it and and happen that some of (more...)

Transforming Missing Data using Oracle Data Mining

In a previous post I showed how you can normalize data using the in-database machine learning feature using the DBMS_DATA_MINING.TRANSFORM function.  This same function can be used to perform many more data transformations with standardized routines. When it comes to missing data, where you have some case records where the value for an attribute is missing you have a number of options open to you. The first is to evaluate the degree of (more...)

Examples of using Machine Learning on Video and Photo in Public

Over the past 18 months or so most of the examples of using machine learning have been on looking at images and identifying objects in them. There are the typical examples of examining pictures looking for a Cat or a Dog, or some famous person, etc. Most of these examples are very noddy, although they do illustrate important examples.

But what if this same technology was used to monitor people going about their daily lives. (more...)

The DaAnalytics Blog has moved.

The DaAnalytics Blog will re-launch on the DaAnalytics website - http://daanalytics.nl/blog. This means that the old blog on http://blog.daanalytics.nl and / or http://obibb.wordpress.nl will remain online but won't be maintained anymore. As from now all new content will be posted on the DaAnalytics website blog. #blog #daanalytics #newstart

Connecting Go Lang to Oracle Database

It seems like more and more people are using Go. With that comes the need to  access a database or databases. This blog will show you how to get connected to an Oracle Database and to perform some basic operations using Go.

The first thing you need is to have Go installed. There are a couple of options for you. The first is go download from the Go Lang website, or if you are (more...)

HiveMall: Transform Categorical features to Numerical

HiveMall is a machine learning library that sits on top of Hive and provides SQL interface to wide range of data preparation and machine learning algorithms.

A common task faced for many machine learning exercises is to convert the data from the format it is captured in (raw data) into a format that is required by the machine learning algorithms. Most ML tools will either have functionality built into the algorithms to do this automatically (more...)

Migrating Python ML Models to other languages

I’ve mentioned in a previous blog post about experiencing some performance issues with using Python ML in production. We needed something quicker and the possible languages we considered were C, C++, Java and Go Lang.

But the data science team used R and Python, with just a few more people using Python than R on the team.

One option was to rewrite everything into the language used in production. As you can imagine no-one wanted (more...)

Machine Learning with Go Lang

Recently I’ve been having a number of conversations with people in several countries about using Go Lang for machine learning. Most of these people have been struggling with using Python for machine learning and are looking for an alternative that will give them better performance. We have been experimenting with C++ and Go Lang to see what the performance differences are. Most of these are with the execution of the ML code. This is great (more...)

Machine Learning Tools and Workbenches

The following is a list of the most commonly used tools and workbenches for machine learning. These are specific to machine learning only. This list does not include any library or frameworks. These are tools and workbenches only. Most offering machine learning tools will include the following features:

  • Easy drag and drop capabilities
  • Data collection
  • Data preparation and cleaning
  • Model building
  • Data Visualization
  • Model Deployment
  • Integration with other tools and languages

As more and more (more...)

Time Series Forecasting in Oracle – Part 2

This is the second part about time-series data modeling using Oracle. Check out the first part here.

In this post I will take a time-series data set and using the in-database time-series functions model the data, that in turn can be used for predicting future values and trends.

The data set used in these examples is the Rossmann Store Sales data set. It is available on Kaggle and was used in one of their competitions.

(more...)

Game of Thrones Series 8: Real Time Sentiment Scoring with Apache Kafka, KSQL, Google’s Natural Language API and Python

Game of Thrones Series 8: Real Time Sentiment Scoring with Apache Kafka, KSQL, Google's Natural Language API and Python

Hi, Game of Thrones aficionados, welcome to GoT Series 8 and my tweet analysis! If you missed any of the prior season episodes, here are I, II and III. Finally, after almost two years, we have a new series and something interesting to write about! If you didn't watch Episode 1, do it before reading this post as it might contain spoilers!

Let's now start with a preview of the starting scene of Episode (more...)

Democratize Data Science with Oracle Analytics Cloud – Predictions and Final Considerations

Democratize Data Science with Oracle Analytics Cloud - Predictions and Final Considerations

Oracle Analytics Cloud as an enabler for Data Science: this is the third post of a series which started with Episode I where we discussed the path from a Data Analyst to a Data Scientist and how to start a Data Science journey with OAC. Episode II was focused on Feature Engineering, Data Analytics and Machine Learning, showing how those steps can be performed in OAC using a visual and easily understandable interface.

This last (more...)

Democratize Data Science with Oracle Analytics Cloud – Data Analysis and Machine Learning

Democratize Data Science with Oracle Analytics Cloud - Data Analysis and Machine Learning

Welcome back! In my previous Post, I described how the democratization of Data Science is a hot topic in the analytical industry. We then explored how Oracle Analytics Cloud can act as an enabler for the transformation from Business Analyst to Data Scientist and covered the first steps in a Data Science project: problem definition, data connection & cleaning. In today's post, we'll cover the second part of the path: from the data transformation (more...)

Democratize Data Science with Oracle Analytics Cloud

Democratize Data Science with Oracle Analytics Cloud

In the last few weeks, I had the chance to speak both at Analytics and Data Summit held in Oracle HQ in San Francisco and OUG Norway Spring Conference 2019 on a wavy cruise between Oslo and Kiel. The underlying topic of the presentations was just one: demonstrate how Oracle Analytics Cloud can be used to bridge the gap between Business Analysts and Data Science.

Imagine: you only need to connect to the data, and (more...)

Announcing The Kafka Pilot with Rittman Mead

Rittman Mead is today pleased to announce the launch of it's Kafka Pilot service, focusing on engaging with companies to help fully assess the capabilities of Apache Kafka for event streaming use cases with both a technical and business focus.

Our 30 day Kafka Pilot includes:

  • A comprehensive assessment of your use cases for event streaming and Kafka
  • A full assessment of connectors
  • Provides a transformation from your current state to future state architecture
  • Delivers (more...)

Oracle Analytics Cloud (OAC) training with Rittman Mead

Rittman Mead have today launched it's new Oracle Analytics Cloud (OAC) Bootcamp. Run on OAC, the course lasts four days and covers everything you need to know in order to manage your Cloud BI platform and assumes no prior knowledge up-front.

As the course is modular, you are able to choose which days you'd like to attend. Day 1 covers an OAC overview, provisioning, systems management, integration and security. Day 2 covers RPD Modelling and (more...)