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How difficult is real time?

How difficult is real time?

27/06/18 | by: Jack Blogg

To people who don’t understand computing, it seems easy. Everything is bound to happen in real time, it’s computing and it’s all connected, right?

For the IT professional in the public sector, whose resources are constrained at the best of times, can be forgiven a little shudder at this. Different data streams have to be standardised and made interoperable while all the time government adds to the mountain of data through tax collection, pension calculations, traffic regulation…the list is virtually endless.

This is reminiscent of an issue facing the IT industry overall, called “Big Data”. It involves a lot of data being pulled together to work with itself and after a process of analytics, allow predictions and forecasting of what’s going to happen.

Techniques from this can be deployed in managing real time applications for the public sector. Two technologies can come into play to ensure it all works: modelling and simulation, and language analytics.

Modelling and simulation does what the name suggests; it builds up patterns of what’s happened in the past and maps them onto future projections. So in a public safety scenario, for example, a local authority might have a look at what happened when a moderately popular football match took place in an area and assess the impact of a top league came by simulating crowd control. Language analytics is important because much of the data input will be from people putting notes on social media, sending random emails and soforth; English may or may not be their first language and a system will need to pick up nuance as well as literal meaning.

The specific technologies that can analyse these elements include cognitive systems, predictive analytics and blockchain technology to distribute the workload around an environment. The storage technology underpinning all of this is a vital component, and S3’s offerings around software defined storage (SDS) lend themselves to the task perfectly.

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