Žiga Vaupot's Blog

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

Oracle Analytics 5.9 has been released and it is time to check some of the new features and functionalities. From what I’ve been able to observe and test by now, the main focus in the new release is in  tighter Oracle Machine Learning (OML) integration, additional database analytics nodes in data flows and improved data visualisations features, there are some details that have been addressed and are nice improvements, but I find them quite useful. Data Visualization and support for new map types One of our customers, Surveying and Mapping Authority of the Republic of Slovenia (GURS) has been publishing various maps and map layers for all sorts of analyses and presentations using Web Map Services (WMS) for years now. They have been using Oracle Business Intelligence and are looking to migrate to the latest Oracle Analytics Server release. You can imagine, that they produce a lot of maps, as this is their core business, but up until now, they haven’t been able to use those maps with Orac

A couple of months ago, I posted a blog Using Oracle ADW machine learning models in Oracle Analytics in which I described how can an Oracle Machine Learning Model from Oracle database can be used in Oracle Analytics. In previous release of Oracle Analytics, Register ML Model option was introduced. This gives users in Oracle Analytics option to register and use a machine learning model that was prepared in Oracle Autonomous Database.  So, let’s begin with a new ML model registration. Registering ML Model with Oracle Analytics In the top bar, next to the Create button, there is an option, Register ML Model, available from the Page Menu. After connection is selected, ML model needs to be selected. We will work with GLM_HOUSING machine learning model: The registration process is really straight forward. However, when registering a new ML Model with Oracle Analytics, that ML Model in previous releases acted as a black box. There were no statistics available to the user, hence no one could

Together with my colleague Mojca Gros of Qubix, we have delivered webinar session with the title Oracle Analytics: Tips & Trick at this year's SIOUG annual conference MakeITWeek2020. In a record time, Oracle Analytics has undergone a transformation from a “old-fashioned” business intelligence tool to a modern, mature, and comprehensive analytics platform that runs in the cloud or, if you want, on a user’s server. New functionalities are being introduced on a very frequent basis, and these are very difficult to keep track of. Be it in the field of data visualisation, data preparation for analysis, machine learning or infrastructure changes. With the development of projects, we clearly face many challenges, which also affect how to plan business analytics solutions.  Webinar discusses some of the challenges and some Tips & Trick that are now available. We are talking about and demonstrating: data discovery and explainability data preparation and enrichment data visualisation