Dette er CodeArts blog. Vi deler thought-leadership og tekniske tips og tricks - men som regel på engelsk.
Automatically tagging your content with topics from a known, well described topic base like Wikipedia can have many cool uses. You can organize your content, suggesting keywords and outbound links, not to mention that you can build up interest profiles of your visitors. These interest profiles can the be used to suggest appropriate content and keep your visitors engaged. Inspired by Episerver Content Intelligence and a couple of earlier projects I've done in the past, I decided to perform an experiment to see how far I could get with a DIY approach as opposed to the traditional cloud-based NLP/AI.
The best thing about Episerver is the community and all the great contributions coming from it. Many of them make it into packages on the Episerver nuget feed - along side Episervers own packages. I have for a long time worked on building tools to explore and visualize this more - and now I'm finally ready to one-by-one share some of the tools coming out of it.
I recently got a chance to try out Episervers latest acquisition, Idio, right here on codeart.dk. In this post, I'll share my first impressions.
Powerslice might have a few years on it's back, but it's still a great editorial tool, when you are working with large amounts of content and have access to Episerver Find. Here's an example of a recent slice I made that let's editors easily identify unused blocks.
Profile Store, Insight, Tracker, Advance - Episerver offers a myriad of different (but connected) REST services for managing and tracking your visitors and prospects. It can be slightly confusing at first - and some of the documentation might be a tad misleading - but once you get the hang of it, they are really powerful tools. I've recently had a chance to explore them in depth. Here is what I've learned so far.
I recently discovered publicwww.com a cool service that lets you search for any text in the html/css/js of all it's 550 million (2019-05-09) indexed web pages, including the cookies sent out and the http header. In this post I put my Episerver goggles on and had some fun with this data.
Some times you have so much great content on your website that you just wish you had a librarian to let your visitors know what to read next. And with Episerver Advance (Content Recommendations) you can at least have something that comes pretty close. I have been lucky enough to try it out on my site.
You don't always have to go the full AI route to get AI like results. In this blog post I'll describe an approach I've used several times (and for multiple purposes) with pretty decent results. Instead of classification algorithms, deep learning or neural networks I'll just simply query my favorite search engine.
In 3 previous blog posts I compared various azure storage technologies with regards to performance and scalability in typical web usage scenarios. I was actually done with the series, but with all that interesting data, I decided to throw my current favorite search/storage/no-sql technology into the mix to get an idea about how it all compares. So - ElasticSearch enters the competition!
This is the 3rd post in my Azure Storage Performance comparison. So far we've examined the typical scenario of storing/retrieving data that most dynamic websites of today deal with. In this post, we'll take a closer look at Update and Delete - and finally review the financial aspects.
In this second post of my performance series looking at Azure storage we're going to take a good look at Read speeds for the various storage types.
Almost every project has some data you want to persist, then read, search through, update and eventually delete. With Azure there are loads of great possibilities - for example Blob Storage, Table Storage, CosmosDb, SQL Azure. I've decided to do some simple and fairly naive tests to compare these for some typical usage scenarios and see how they perform.
