Book Image

Building Websites with VB.NET and DotNetNuke 4

Book Image

Building Websites with VB.NET and DotNetNuke 4

Overview of this book

DotNetNuke is an open-source Content Management System and web application framework. DotNetNuke has taken the Microsoft world by storm and now at version 4, its community has grown to over 200,000 users. This book covers virtually everything you need to know to get your DotNetNuke website up and running. Concisely written and with clear explanations, this book is covers installation, administration, deployment, site creation and all of the basic built in DotNetNuke modules. For developers, chapters on the core architecture, skinning and custom modules, including coverage of the DAL+, give you the skills to customize and extend your site. The book starts off by giving you a deep understanding of working with basic DotNetNuke sites, guiding you through the features and giving you the confidence to create and manage your site. After that, you will journey to the heart of DotNetNuke, and learn about its core architecture. Always concise, relevant and practical, you will find out what makes DotNetNuke tick, and from there, you will be ready to customize DotNetNuke. Developers will enjoy the detailed walkthrough of creating a new custom modules. Special emphasis is given to the DAL+, an extended feature set of the DotNetNuke Data Access Layer (DAL). You will see how to create custom modules with the DAL+, and invigorate your module development. Web designers will enjoy the material on skinning, helping them to create a new look for their site. You will learn about creating new skins, and packaging them up for easy deployment. You will master all of this as you leap into the development of a DotNetNuke 4 site.
Table of Contents (15 chapters)
Free Chapter
1
Section 1 – Introduction to Graph Machine Learning
4
Section 2 – Machine Learning on Graphs
8
Section 3 – Advanced Applications of Graph Machine Learning

Dealing with large graphs

When approaching a use case or an analysis, it is very important to understand how large the data we focus on is or will be in the future, as the dimension of the datasets may very well impact both the technologies we use and the analysis that we can do. As already mentioned, some of the approaches that have been developed on small datasets hardly scale to real-world applications and larger datasets, making them useless in practice.

When dealing with (possibly) large graphs, it is crucial to understand potential bottlenecks and limitation of the tools, technologies, and/or algorithms we use, assessing which part of our application/analysis may not scale when increasing the number of nodes or edges. Even more importantly, it is crucial to structure a data-driven application, however simple or at early proof of concept (POC) stages, in a way that would allow its scaling out in the future when data/users would increase, without rewriting the whole application...