Žiga Vaupot's Blog

Oracle Analytics 6.3 is bringing updated Developer Reference feature that is now directly accessible when creating an analysis in a workbook. Developer Reference has already been available before with JSON or XML generated code of visualization objects, code snippets for embedding visualizations in other portals, and other. With built-in Performance Tools can be now used to analyze statistics such as query time, server time, and streaming time for visualization components in workbooks. Users can now review the logical and physical queries and check the execution log. In order to access  Performance Tools  under  Developer option  from the  Menu ( top right corner ). Developer option has been, as I said, known from before, but now it includes the Performance Tools section which provides developer quite useful information about visualisation execution performance. But be aware,  Developer  option in the  Menu  is not enabled by default. It has to be enabled. This can be done by naviga

Recently I wanted to do a short demo on how to use Time Series functions in My Calculations in Oracle Analytics , more specifically how to create a new calculation using Ago function in Data Visualization workbook . Very straightforward, one would say, however, it is not.  There is a known bug , quite well documented in Oracle Support, however, let me describe it briefly and provide a short workaround. Let’s start with this simple report: Now, let’s create a new calculation that uses AGO function, which has the following notation: Ago(<<Measure>>, <<Level>>, <<Number of Periods>>) There is no secret about that, right? Well, there might be some … As I mentioned, when you create a new calculation in Data Visualization workbook, you need to be careful when typing the <<Level>> part of the formula. For example, if you follow the standard notation, then the formula should look like this: Note: in the formula above I added extra space betwe

Just a few night ago our Oracle Analytics instances have been upgraded to the latest version 6.3. In general 6.3 doesn't contain major new features, however there are still some worth mentioning. Workbooks instead of Projects The one that is the most obvious is that we are no longer creating Projects in Data Visualisation, but we work with Workbooks .  Developer Reference During the development process, if you wanted to observe and analyse performance of reports and analyses, you had to navigate to Console and search for your report performance under Session and Query Cache . This is no longer necessary in 6.3. Developers can now review performance statistics for visualisation components right in the Visualize canvas.  You can now analyse statistics such as query time, server time and streaming time for all visualisation components in workbook. Calculate and display Lift and Gain for predictive models Another interesting new feature is an option to evaluate and visualise machine

In my previous post I described, in a bit more details, how to perform graph analysis in the case of Node Ranking. Basically the key tool that you can use in Oracle Analytics is Data Flows . Graph Analytics step in Data Flows enables users to perform four graph analytics operations. Besides Node Ranking these are Sub Graph , Clusters and Shortest Path . For easier understanding and visualisation we are using the following Dolphins dataset. Sub Graph Sub Graph operations finds all nodes within specified number of hops of a given node . Using other words, Sub Graph finds all nodes, neighbours of a given node, if we specify the number of hops is one. If number of hops is two, Sub Graph returns all neighbouring nodes of a given node and all neighbours of found neighbours, and so on. This is for example useful in marketing when we can find who are friends of a customer who has bought a specific product. We might assume that customer presented that product to his friends and is also pos

Node Rank Graphs describe relationships between various entities in a dataset. When defining graphs, then nodes correspond to entities and edges represent relationships between them. Node Ranking measures the importance of nodes in a graph.  For example, in social networks we have people. We represent people, members of a social network, as nodes. The relationships, e.g. friendship, are represented as links between two people. People with a lot of friends are often called influencers, as their decisions, opinions, etc. are exposed to more people than other members of the network. Their rank is higher. Marketing departments tend to focus on and work with these influencers in order to get the maximum reach within their networks. Measure that measures this phenomena is called Node Rank .  We can find similar use cases practically everywhere. In transportation, for instance, the network of airports has more and less important airports. Airport hubs are those airports which have the most c

You can find the following definition about Graphs and Graph theory on Wiki :  In mathematics, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices (also called nodes or points) which are connected by edges (also called links or lines). A distinction is made between undirected graphs, where edges link two vertices symmetrically, and directed graphs, where edges link two vertices asymmetrically. Graphs are one of the principal objects of study in discrete mathematics. In essence, graphs are used to model various types of relationships. In case of business analytics, graphs can used in sales and marketing departments to perform recommendations of the products to particular group of customers that are somehow related to some other group of customers, in manufacturing they can be exploited to manage inventory more effectively due to better planning of materials and semi-produc

A few months ago, I was writing about Market Basket Analysis that you can perform in Oracle Autonomous Database and Frequent Itemsets that can also be created with Oracle Analytics .  When working with Oracle Analytics, one important part of Market Basket Analysis was missing - Association Rules and Recommendations . Oracle Analytics from version 6.0 supports this feature, therefore complete Market Basket Analysis can be done now. Let's take a look at the data flow we created for creation of Frequent Itemset. It consists of three steps: As usual, we begin a data flow by reading data. In our case we read database data table CARBO_TRANSACTIONS. This table has relatively simple structure: For our analysis only BASKET and UPC columns are important. In the second step, Database Analytics step with analytics operation Frequent Itemsets we define parameters for the algorithm. In the Outputs , we define output columns. In our case we are looking only for combinations of the two product