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BigData News Monday, June 4

Urbanization, the Escalator Dilemma and Big Data – The News Lens International Edition

  • But many large cities face the twin challenges of ageing infrastructure and increased volumes of people flowing through transport hubs.
  • With the introduction of ticketless transport cards its now possible to gather more data about people flow through busy transport hubs as we tap on and off.
  • Tracking commuters in-station journeys through their Wi-Fi enabled devices, such as smart phones, can also offer a detailed picture of movement between platforms, congestion and delays.
  • Using data analytics, people movement specialists identify movement patterns, count footfall and analyse commuter behavior.
  • The application of data analytics to people flow and its use by the people movement industry to achieve efficiencies needs careful scrutiny to ensure benefits beyond commercial gain.

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Urbanization, the Escalator Dilemma and Big Data – The News Lens International Edition

​Machine learning: Making it work in the real world

  • See: Special report: How to implement AI and machine learning (free PDF) – – Kay says Dentsu Aegis is using a range of machine-learning technologies in this process.
  • Corbridge says the Trust’s first foray into machine learning will be a robotic process-automation development which aims to make it easier to find the right medical records among hundreds of scanned documents, saving time for clinicians.
  • Rob McLaughlin, head of digital decisioning and analytics at Sky, says the television and telecommunications specialist is using AI and machine learning as part of a leading-edge approach to data that uses advanced technology to augment human expertise.
  • See: Big data in action: AI, machine learning, cloud, IoT, and more – – Companies choosing between machine learning and AI need to think about whether they’re using data to make big, strategic decisions or micro, customer-level decisions.
  • However, companies that are obtaining competitive advantage with AI and machine learning are thinking more strategically about their business model and product innovation.

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​Machine learning: Making it work in the real world | ZDNet

Machine Learning is Everywhere, What’s the Difference?

  • But not all analytics and machine learning are industrial-strength and few can help the worlds largest, most important industrial companies drive best-in-class performance.
  • Not quite; were talking machine learning for asset performance management; with potentially messy and incomplete data and requirements to collect from real-time data sources not data in a file.
  • It must work fast, inline, in real-time, all the time giving accurate advice when machine and process behaviors indicate precisely know failure patterns and deviations from normal behavior both.
  • Additional to machine learning, such prescriptive guidance is based on established root cause analysis and presents information on the approach that will proactively avoid process conditions that cause damage and/or advise on the precise maintenance required to service the asset.
  • Make it work for Joe Normal – – – Build into that framework an abstraction mechanism so that engineers who understand the problems can exercise the machine learning without intense data science skills and now you have an application that fits precisely with the work processes and skill sets available…

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Machine Learning is Everywhere, What’s the Difference? | ARC Advisory Group