Žiga Vaupot's Blog

My SQL beginnings It was approximately 35 years ago (gosh, that long!?) when I first encountered SQL—Structured Query Language, for those who might not be 100% sure what I am talking about. It happened during labs at faculty when we visited IBM's training center in Radovljica, Slovenia, to learn and experiment with this (at least for me) new programming language. They called it a 4th generation language. With its roots in Boolean algebra and simple structure, we were told that it was almost like natural language (English, of course—who would have thought one day you would use Slovenian to query databases?). With all those SELECTs, FROMs, WHEREs, GROUP BYs, HAVINGs, ORDER BYs, etc., it actually sounded really cool and easy. My Select AI beginnings Fast forward to today, the other day I finally found some time to explore something different from my usual daily work. What caught my attention was one of the latest Oracle blogs: Introducing Select AI - Natural Language to SQL ...

Analytics and Data Oracle User Community ( AnDOUC ) Winter TechCast Days  took place between 15th and 17th Februrary 2022.  I am very honoured to make a small contribution at the  Machine Learning Day  where I presented my view on what business users should know about machine learning support in Oracle Analytics, presentation with the title  Business Users and Machine Learning in Oracle Analytics . Business Users and Machine Learning in Oracle Analytics Traditionally, the use of machine learning is in the domain of the data scientist. The latter has in-depth knowledge of machine learning methods and algorithms and strives for the most optimal preparation of machine learning models, which are then used for various analyses such as predictions, customer segmentation, anomaly detection, finding patterns and the like. Because the machine learning process usually takes a long time, even a few weeks, the data scientist is a rather “rare species”. Business users usuall...

Charlie Berger and Abi Giles-Haigh ran the hands-on lab session yesterday (11th May 2021) at Analytics and Data Oracle User Community's, AnDOUC ( https://analyticsanddatasummit.org/ ), TechCast Days, Spring Sessions with the title A Two-Step process for Detecting Fraud . I was invited to help with the HOLs alongside with Tim Vlamis and Edelweiss Kammermann .  The hands-on lab is about taking users through Oracle Machine Learning (SQL) Notebooks while using multiple use case scenarios for analysing insurance claims data and customer behavior data. Students were running the notebooks themselves to build and apply OML models inside Oracle Autonomous Database and then worked with Oracle APEX applications that leveraged OML’s insights and predictions ... So basically, what we did was: we took the Insurance Claims data set and ran 1-Class SVM algorithm, which is used for anomaly detection in order to identify suspicious claims.  For business (and any other) users we have an option ...

Frequent Itemsets and recommendations are two components of Market Basket Analysis. Everything is prepared within Oracle Autonomous Data Warehouse (ADW) and brought into Oracle Analytics where frequent itemsets and recommended items are visualised using Sankey and Network graphs.

Introduction Recommendations are usually always there to stimulate you to buy additional products or services regardless if you are shopping online or in a store. Sometimes these recommendations are just intuitions of sales manager, but more often these are prepared using some machine learning algorithm, for example Association Rules. When Association Rules modeling is applied to transaction sales data, the model is called Market-Basket Analysis. Market-Basket Analysis can be used in various situations, from direct marketing, sales promotions, discovering business trends, but also for effectively managing store layouts, catalog design or for exposing cross-sell opportunities in a web store. Since we I am not going to discuss theory about Market Basket Analysis and Association Rules, please find more information here:  https://docs.oracle.com/en/database/oracle/oracle-database/19/dmcon/data-mining-basics.html, https://docs.oracle.com/en/database/oracle/oracle-database/19/dmcon/assoc...