As hub-and-spoke distribution models have helped revolutionize countless sectors, their translation into digital architectures is making significant inroads into data management for the modern company. Simply put, a hub-and-spoke model consists of a centralized architecture connecting to multiple spokes (nodes). It makes sense that this is considered the ideal paradigm […]
How a Hub-and-Spoke architecture can help manage data. Read More
By the end of 2018, there will be 11,2 billion connected things in use around the world, reflecting a staggering growth over the past couple of years. The forecast does not include smartphones or computers; it refers to all sorts of smart devices designed to help businesses and consumers work
What’s the deal with the Internet of Things? Read More
Since it’s revival in 2006, Deep learning advanced so quick that is used in many aspects of modern society such as: Deep Learning Analytics Companies such Google and Microsoft have access to large volumes of data, social media organisations such as Facebook, YouTube and Twitter have billions of users that
Deep Learning Application Areas – Pattern Recognition Read More
As seen in the last post, after the event of CNN’s (Convolutional Neural Networks) we step in the second AI Winter, but in 2006 this would change… 2006: Deep Learning Arises In 2006, Hinton, Simon Osindero and Yee-Whye Teh published a paper where they solved the vanishing or exploding gradient problem. In
A Deep Learning history walk through – Deep Learning Arises Read More
Deep Learning is one the last iterations on the evolution of Machine Learning. One evolution that started with something as simple as Linear Regression, and despite all the difficulties and winters that grassed for, it has been an area whose advances have generated a tsunami that has waves that reached
A Deep Learning’s history walk through Read More
Edge computing takes localised processing a bit further than Fog Computing, because it allows for actions to be taken on-site, in the processing point. This poses an advantage over Fog Computing as there are less points of failure. Each item in the chain is more independent and capable of determining
Even though Cloud computing is a great way of processing the data generated by the “things”, it doesn’t meet all IoT’s needs. For instance, one issue that affects the quality of service (QoS) severely is network latency. Real time applications are affected by the delay caused by latency in networks
In the Lambda Architecture website we have a brief history and description of the architecture. “Nathan Marz came up with the term Lambda Architecture (LA) for generic, scalable and fault-tolerant data processing architecture, based on his experience working on distributed data processing systems at Backtype and Twitter. The LA aims
The definition of Cloud computing provided by the National Institute of Standard and Technologies says: ‘‘Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing re-sources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with
The first time someone used the term “Internet of Things” was in 1999 by Kevin Ashton in a presentation at Procter & Gamble [1]. Since then, the steady progress of technology has produced an array of low-cost sensors and the growth of computer processing power sustained by Moore’s Law. That