Žiga Vaupot's Blog

I recently returned from Reading, UK, where I had the opportunity to attend the UKOUG 2023 conference . This year's event was particularly engaging, commemorating the 40th anniversary of the UK Oracle User Group. My participation at the conference had a dual purpose. Firstly, I attended to present, and while I was there, I had the chance to join several presentations that piqued my interest. I'm genuinely impressed by the innovative solutions showcased and the quality of work demonstrated by the presenters and their teams. My winners My standout presentation was LLMs are the Future of Conversational AI  by Antony Heljula of TPXimpact. Antony shared his experiences in developing chatbots that leverage large language models in conversational AI. Truly groundbreaking! A close second for me was Gianni Ceresa's (DATAlysis) presentation titled ID Please: Did You Already Ask That to Your Data?  Gianni delved into the crucial aspects of data lineage and governance, addressing the...

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 t...

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-pr...