Google Yolo and Spring Boot 2.0 Authentication

Back in 2016, Google announced the "Open Yolo" project: You Only Login Once. It originally seemed to be an Android library but during Google's last Dev Summit in October 2017, Google released "One-tap Sign-ups On Websites and API Integrations" which brings Google Yolo to your website via JavaScript goodness. There's a very easy guide that … Continue reading Google Yolo and Spring Boot 2.0 Authentication

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Developing Alexa Skills

In this article we'll explore how to add some custom skills to your Alexa powered device. The material in this blog post was tested on an Echo Dot - however Amazon recently enabled Alexa on most Android powered smartphones so the barrier for entry to developing these skills has been lowered significantly. I actually ended … Continue reading Developing Alexa Skills

Analyzing credit card transactions using machine learning techniques – 3

Introduction In a previous article, we explored how PCA can be used to plot credit card transactions into a 2D space, and we proceeded to visually analyse the results. In this article, we take this process one step further and use hierarchical clustering to automate parts of our analysis, making it even easier for our … Continue reading Analyzing credit card transactions using machine learning techniques – 3

Analyzing credit card transactions using machine learning techniques – 2

Principal Component Analysis - Introduction and Data Preperation Principal Component Analysis [PCA] is an unsupervised algorithm which reduces dimensionality and is widely used. A good visual explanation can be found here: http://setosa.io/ev/principal-component-analysis/ As mentioned in our previous article, Correspondence Analysis  works exclusively on categorical data. In contrast, PCA accepts only numerical data. This means our data … Continue reading Analyzing credit card transactions using machine learning techniques – 2

Analyzing credit card transactions using machine learning techniques

Introduction In this 3-part series we'll explore how three machine learning algorithms can help a hypothetical financial analyst explore a real data set of credit card transactions to quickly and easily infer relationships, anomalies and extract useful data. Data Set The data set we'll use in this hypothetical scenario is a real data set released … Continue reading Analyzing credit card transactions using machine learning techniques

Nugget Post: Reactive Functions to parse nested objects

Note this article assumes familiarity with the Observer Pattern / Reactive Programming as described here: http://reactivex.io/ Some APIs return complex nested JSON objects. For example, take this cleaned up sample response from ElasticSearch (which incidentally is used to build the "Data Table" visualization): https://gist.github.com/dvas0004/8f3427955a5bb21213c864d30094d072 Note the structure of the object. Within the top level "aggregations" object … Continue reading Nugget Post: Reactive Functions to parse nested objects

Lessons Learned: Winlogbeat & Forwarded Events – no event description

Scenario: Shipping Azure Cloud Logs to an Elasticsearch Cluster The Azure Log Service [AZLog ] audits events across your Azure Cloud infrastructure, and sends these to a central log collector. It leverage the Windows Event Forwarding subsystem to do this, meaning that the collector server will be able to view the AZLog alerts via the … Continue reading Lessons Learned: Winlogbeat & Forwarded Events – no event description