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Rick van der Lans

Welcome to my blog where I will talk about a variety of topics related to data warehousing, business intelligence, application integration, and database technology. Currently my special interests include data virtualization, NoSQL technology, and service-oriented architectures. If there are any topics you'd like me to address, send them to me at rick@r20.nl.

About the author >

Rick is an independent consultant, speaker and author, specializing in data warehousing, business intelligence, database technology and data virtualization. He is managing director and founder of R20/Consultancy. An internationally acclaimed speaker who has lectured worldwide for the last 25 years, he is the chairman of the successful annual European Enterprise Data and Business Intelligence Conference held annually in London. In the summer of 2012 he published his new book Data Virtualization for Business Intelligence Systems. He is also the author of one of the most successful books on SQL, the popular Introduction to SQL, which is available in English, Chinese, Dutch, Italian and German. He has written many white papers for various software vendors. Rick can be contacted by sending an email to rick@r20.nl.

Editor's Note: Rick's blog and more articles can be accessed through his BeyeNETWORK Expert Channel.

I hadn't done anything with graph theory and graph analytics for quite some time until I wrote a technical whitepaper on the graph database server InfiniteGraph. After doing some research and studying the product I came to the conclusion that I had neglected this topic. Graph analytics is a powerful form of analytics that allows us to analyze data in a way that's not possible with other tools. In fact, tools for graph analytics can be seen as complimentary to all the reporting and analytical capabilities we are all so familiar with.

 

When writing the paper I talked to several people, and quite a number didn't see why graph analytics is special, nor did they think it would be relevant for many organizations. But that's not the case. All kinds of organizations can benefit from graph analytics. For example, in a government organization a graph can be created linking all private persons and organizations and graph analytics can be used to find 'hidden' relationships between organizations. In the financial world, it can be used to 'follow' money transfers to create a trail, and in transport it can be used to find the shortest route to deliver goods to various addresses. Every organization that logs all the traffic on their website can create a graph that shows how individual visitors travel through the website. This traffic can be simulated to determine whether visitors are using the correct and the most ideal path. The most obvious example is that graph analytics is used to find central members in a social network. And the list goes on.

 

Various tools are available that can do graph analytics and that can show the results graphically. Unfortunately, these tools can't handle large graphs made up of millions of nodes and relations. This is where graph database servers come in. Today, they do make online graph analytics on massive graphs possible.

 

In business intelligence architectures, graph database servers can be used for building data marts designed specifically for graph analytics. These data marts will receive their data from a central data warehouse. In a way, this is comparable to developing an MDX-based data mart for users needing more classic forms of analysis.

 

In a nutshell, if you haven't studied graph analytics and the associated tools and database servers for some time, just like me, take some time and dive into it. It's exciting technology!


Posted September 29, 2010 6:56 AM
Permalink | 1 Comment |

1 Comment

Great point!
Educators have figured out that kids learn all different kinds of ways (Gardner's theory of Multiple Intelligences, etc.) - it stands to reason that the same is true for us grownups.
Hope you'll provide more insights into graph analytics - and other ways in which data can be better visualized - in future posts!

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