We’ve helped a number of clients to develop and deliver rankings - of healthy places, of “cosy” areas of the country, of corporate wellbeing, and of universities. Building a ranking sounds like an easy job, since the output is just a list. But building a ranking well and meaningfully is much harder than you’d think, because every individual on the list is keen to be in exactly the right place, or even higher up, and is going to pick apart every choice you made in the process.
The Kixi platform is constructed around a design pattern known as Command Query Responsibility Separation (CQRS). This pattern is part of a hierarchy of designs that build upon one another and are generally used within a Micro-Services architecture.
Over the past couple of months I’ve been using the Onyx Platform with Mastodon C an awful lot.
So this short series I’m going to show you how to build a Onyx Kafka application that consumes a Kafka stream and kicks out the messages to the console. Nothing flashy but more a “here’s the concepts”.
Earlier this year we built a web-based interface for London’s local authorities (known as ‘boroughs’) to run their own population projections. Six months on we review their experience.
This is a senior role, based either in our London office or at your remote location within the UK. The base salary for this role will be between £50-60k pa. We are happy to consider part-time or flexible working proposals.
Very sadly, our fantastic Delivery Manager Elisabeth is moving away. However, this means that we have a very exciting vacancy!
We did some interesting work a while ago with the UK’s Department for Environment, Food, and Rural Affairs, which tested the possibility of using text mining to highlight upcoming risks from incoming reports from the field.
In this blog post, we’ll dig into some of the technical details of how to do this, and why it’s interesting. A longer case study of the project is here.
At Mastodon C we’re always keeping an eye out for new libraries or data science techniques which might help us make our models faster or smarter. We very often need to work with large collections of text documents for our clients - all sorts of very useful insights can be extracted from the unstructured material that gets collected all the time in reports, notes, emails, or support tickets, as long as we can figure out how to put it to work.
We’ve been hard at work since our last update - in that time we’ve delivered our first release of Witan, and two babies! Our city modelling platform is now live and our first users in London have been given access this week.
Living in York for the majority of my life, I’ve got used to flooding. With the number of floods increasing, and the probability of that flood causing long lasting damage also increasing, it would be a good idea to start peeking at past data and seeing if there’s anything jumping out at us.
Subscribe via RSS