Žiga Vaupot's Blog

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 to do

  Oracle Analytics 6.0 has arrived! Our Oracle Analytics instance was just patched a few hour ago. Everyone who is using Interactive Dashboards and Analytics (even from OBIEE times) is familiar with hierarchical columns. They are very popular with users and in some cases, like working with OLAP cubes (ie. Essbase), necessary. Oracle Data Visualisations (in the past) aka Oracle Analytics (today) lacked this functionality until now. In version 6.0 this has finally been included and here we are testing it. Data Visualisations with Hierarchical Columns Hiearchical Columns are the most applicable in tables or pivot tables, however they can be used in some other visualizations like trellis. We will create a project first. Hierarchical columns can be defined in Oracle Analytics Data Models aka Subject Areas, Oracle Planning applications or Essbase cubes. By selecting one of subject areas you can now see hierarchical columns in the data model in the Data panel: Working with hierarchical column

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.

SIOUG (Slovenian Oracle User Group), in cooperation with Qubix , organised the first webinar in the Oracle Analytics - Tips & Tricks Series . What's new in Oracle Analytics 5.9 webinar took place on Thursday, 25th February 2021, starting at 11am CET .  Topics covered: How to prepare data to analyse the products that are most often sold together? How to register and use machine learning models from Oracle database in Oracle Analytics. How to parse text and how to use parsed text in combination with other business data to produce effective and interactive visualisations. How to use existing web mapping services (WMS and XYZ) in analyses using geo-data. What are the key changes and improvements in data visualisation. Webinar was delivered by: Mojca Gros , Qubix, Senior Business Analytics Consultant Žiga Vaupot , Qubix, Country Manager and Oracle ACE Here is the recording of the webinar (language used is Slovenian).

Last night, our Oracle Analytics instance was upgraded to 5.9.  It was long awaited upgrade, which introduces some nice new features and improvements. My personal interest lies with the enhanced Machine Learning support, however, there are several other features that might draw your attention.  Let me briefly talk about some of them. Additional functions in Database Analytics step in Data Flows Two new Database Analytics steps in Data Flows are: Frequent Itemsets and  Text Tokenisation. About Frequent Itemsets , I'm describing in detail in my separate blog  Oracle Analytics 5.9: Frequent Itemsets , so let me not repeat myself here. Text Tokenisation allows users to analyse textual data by breaking it down into distinct words and counting the occurrences of each word.  Technically, when data flow is run, a new database table would be created, table is named  DR$IndexName$I , which contains the token text and the token count related details. Once created, this table can be used to c

In my recent blog Market Basket Analysis with Oracle Machine Learning and Oracle Analytics , I was discussing an option when you create a Market Basket Analysis model with Oracle Autonomous Database and use the result of that model with Oracle Analytics.  Now, with Oracle Analytics version 5.9, Market Basket Analysis has been included within Oracle Analytics. Not all functionality seems to be available, as only Frequent Itemsets analysis is supported, but I was told Association Rules should follow (this blog is using Oracle Analytics 5.9 Early Release version). This really simplifies the process as business users can now start analysing frequent itemsets, combinations of items that are most frequently sold together.  Frequent Itemsets in 5.9 Transactions data Transactions data is stored in Oracle Autonomous Data Warehouse (ADW). Originally these data can be found on  Dunnhumby Source Files  (Carbo-Loading data set): CARBO_TRANSACTIONS database table contains 5M+ transactions and has t