Arrow is showcasing a new project that demonstrates the power and value of large-scale business intelligence and data analytics, by giving an accurate point-in-time consensus on London’s present state of happiness. The How Happy Is London? project ingests and processes freely available, structured and semi-structured data from multiple, unconnected sources and then applies carefully designed algorithms. It processes approximately 2.6 billion real-time data points a day, factoring in data such as Transport for London status updates and weather alerts from the Met Office - while a sentiment algorithm analyses a broad spectrum of words on Twitter that relate to happiness in conjunction with London.
The data is then represented online as a series of images of people and places around the capital and an overall happiness indicator, showing London’s current mood: which fluctuates between a base of ‘business as usual’, through ‘happy’ and ‘life’s good’, up to ‘on top of the world’.
How Happy Is London? demonstrates Arrow’s Business Intelligence portfolio of solutions applied to a real-world challenge: discovering the mood of a city in real-time. The project highlights how the methods employed to transform billions of units of data into a single, easy-to-grasp result can be applied to business decision-making.
Integration?tooling is used to leverage REST-based APIs, allowing a very diverse data set to be ingested quickly to determine its value to the happiness equations. For near real-time streaming data needs, How Happy Is London? uses a Hadoop platform allowing large?quantities?of processing to occur quickly on the semi-structured social data. Data is?stored in?structured databases and then presented to the various users via a?combination of Apache NiFi and API connectivity. The results are presented as a REST API under an open data license, so any?third?party can register and take?advantage?of the Happiness Index for?their?own applications.
David Fearne, technical director, UK & Ireland at Arrow ECS, said, “Understanding human behaviour in relation to a city is about differentiating signals of importance from background noise. The How Happy Is London? project uses deep learning networks to constantly identify important information, and then adapt the use of that data in real-time within our algorithm. This allows us to replace a traditionally static model of London with a representation that is as organic and dynamic as the city itself.”
Eric Nowak, president EMEA at Arrow ECS, commented, “How Happy Is London? is an analytical approach to creating a point-in-time view of the happiness of a global city. It’s inspiring to see big data and analytics come to life in this way, signalling a new chapter at Arrow and in the technology industry. We’re looking forward to developing further advances in other major cities across the EMEA region.”
