Swim Blog

Swim Blog

Three Ways Edge Computing Will Affect the Future of Smart Cities

Posted on Oct 20, 2017 11:24:00 AM by Swim Team in Machine Learning, in Swim ESP, in Industrial IOT, in Smart Cities, in Edge Analytics, in Swim AI

Discussions are occurring in major cities throughout the world between city planners, technologists, and citizens about how to make their city a Smart City. With ambitions for creating a better quality of life for citizens, Smart City planners must tackle a complex landscape of sensors and data processing technologies. Due to the rapid pace of innovation with technologies such as Edge Computing, Machine Learning, and Digital Twins, it’s often hard to keep track of how recent developments will affect the cities of the future.

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Key Takeaways from “The Forrester Wave: Streaming Analytics” (Part II)

Posted on Oct 20, 2017 11:24:00 AM by Swim Team in Machine Learning, in Swim ESP, in Industrial IOT, in Edge Analytics, in Swim AI

This is a part 2 of a two part series. Read more about the future of streaming analytics in part 1.

While the data lakes landscape has matured over the last few years, the streaming analytics market is continuing to develop as more enterprises embrace the use of streaming analytics to provide real-time insights for their business systems. Early returns suggest that embracing streaming analytics in industry can lead to significant efficiency gains and decreased operational costs. For example, a study by McKinsey & Co. found that “using real-time data to predict and prevent breakdowns can reduce [manufacturing] downtime by 50 percent.” With real-world cost savings at stake, the momentum toward real-time has never been stronger.

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Key Takeaways from “The Forrester Wave: Streaming Analytics” (Part I)

Posted on Oct 18, 2017 11:15:00 AM by Swim Team in Machine Learning, in Swim ESP, in Industrial IOT, in Edge Analytics

This is a part 1 of a two part series. 

While the data lakes landscape has matured over the last few years, the streaming analytics market is continuing to develop as more enterprises embrace the use of streaming analytics to provide real-time insights for their business systems. Early returns suggest that embracing streaming analytics in industry can lead to significant efficiency gains and decreased operational costs. For example, a study by McKinsey & Co. found that “using real-time data to predict and prevent breakdowns can reduce [manufacturing] downtime by 50 percent.” With real-world cost savings at stake, the momentum toward real-time has never been stronger.

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Future of Smart Cities and Real-time Traffic Predictions (Part II)

Posted on Oct 13, 2017 11:05:00 AM by Swim Team in Machine Learning, in Swim ESP, in Industrial IOT, in Smart Cities, in Edge Analytics, in Swim AI

This is a part 2 of a two part series. Read more about the future of Smart Cities Part 1.

New innovations in Machine Learning, Edge Computing, and real-time data analytics are shaping the development of Smart Cities projects around the world. One benchmark use case for Smart Cities efforts is that of Intelligent Transportation Systems (ITS), which include the use of connected vehicles and transportation infrastructure to automate or optimize transportation systems. With 58% of in-development Smart Cities projects relating to either Smart Buildings or Smart Transportation efforts, and 250 million connected vehicles expected to be on the road by the year 2020, the adoption of Smart Cities technologies will have a transformative effect on the transportation systems of the future.

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Future of Smart Cities and Real-time Traffic Predictions (Part I)

Posted on Oct 11, 2017 11:00:00 AM by Swim Team in Machine Learning, in Swim ESP, in Industrial IOT, in Smart Cities, in Edge Analytics, in Swim AI

This is a part 1 of a two part series. 

New innovations in Machine Learning, Edge Computing, and real-time data analytics are shaping the development of Smart Cities projects around the world. One benchmark use case for Smart Cities efforts is that of Intelligent Transportation Systems (ITS), which include the use of connected vehicles and transportation infrastructure to automate or optimize transportation systems. With 58% of in-development Smart Cities projects relating to either Smart Buildings or Smart Transportation efforts, and 250 million connected vehicles expected to be on the road by the year 2020, the adoption of Smart Cities technologies will have a transformative effect on the transportation systems of the future.

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Machine Learning vs. Artificial Intelligence - Infographic

Posted on Oct 6, 2017 11:17:00 AM by Swim Team in Machine Learning, in Swim ESP, in Industrial IOT, in Edge Analytics, in Swim AI

Swim ESPTM is a streaming data analytics engine that takes advantage of a distributed network architecture to operate self-training Machine Learning (ML) at the edge in real-timeSwim ESP offers a simple approach to Machine Learning at the edge, which provides real-time  learned insights for time-critical applications. Swim applies learning at the data source – such on industrial devices, Smart City sensors, Unmanned Aerial Vehicles (UAVs), or other autonomous vehicles. Swim ESP then shares learned insights with other virtual components using an efficient pub-sub protocol, automatically building a distributed, secure, & resilient fabric that spreads knowledge throughout the network.

We've put together an infographic comparing Machine Learning and Artificial Intelligence and highlight examples where edge computing solutions like Swim ESP can enable these applications in the future:

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Learn Like Humans: Not Like "Big A.I.”

Posted on Oct 4, 2017 11:36:23 AM by Chris Sachs in Machine Learning, in Swim ESP, in Industrial IOT, in Edge Analytics, in Swim AI

I have a hypothesis: a brain twice as massive as a human brain would only be marginally more intelligent. And a single brain with 7 billion times the mass of a human brain would be orders of magnitude less capable than all of humanity. This feels analogous to Haldane’s famous 1928 piece “On Being The Right Size”, mapped into the domain of learning. 

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Redefining the Edge: Process Data at the Edge

Posted on Sep 29, 2017 12:06:30 PM by Chris Sachs in Machine Learning, in Swim ESP, in Industrial IOT, in Edge Analytics, in Swim AI

What’s cloud computing, and what’s the edge? We could describe boundaries based on the points of demarcation between providers and customers, but that quickly gets messy: AT&T serves my phone but it also serves my company; moreover AT&T is more familiar as a communications provider than as a cloud provider. Let’s try again: In an enterprise context Edge means “on prem”, and Cloud means “not on prem”. Tricky again: Silver Spring Networks is one of the largest providers of Smart Metering Infrastructure, with over 25M endpoints under management (like your house). They operate low bandwidth networks city-wide, and give their utility provider customers accurate information about energy consumption. What’s “on prem” in this story? What’s “on prem” to a vendor of self-driving cars, or to Uber, which eschews on-prem IT infrastructure but receives masses of data from drivers and riders to its cloud hosted apps world wide? 

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Five Ways Real-Time Edge Analytics Can Save the Day

Posted on Sep 28, 2017 2:28:14 PM by Swim Team in Machine Learning, in Stateful Applications, in Swim ESP, in Industrial IOT, in Edge Analytics, in Digital Twin

Swim ESP™ provides edge analytics capabilities for brownfield industrial environments, enabling businesses to extend existing sensor networks into the real-world and leverage the compute power of already-deployed devices. Swim applies Machine Learning at the network edge, taking advantage of computing resources on deployed devices to perform real-time edge analytics. Swim automatically builds a distributed, secure, resilient fabric that enables connected devices to share insights with each other, spreading knowledge through the fabric similar to the way knowledge spreads in the real world.

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Swim Swarm: Digital Twins + UAVs + Machine Learning

Posted on Sep 22, 2017 11:21:00 AM by Swim Team in Machine Learning, in Swim ESP, in Industrial IOT, in Edge Analytics, in drones, in UAV's, in Digital Twin, in Swim Swarm

Swim ESPTM is a streaming data analytics engine that applies machine learning in real-time, taking advantage of a distributed learning architecture to gain efficiency, ensure security and high availability, and to permit rapid and reliable dissemination of learned insights. Swim ESP is self-training and operates in real-time, training and learning continually from data streams.

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