Predixion Software Announces RIOT – IoT Analytics Solution that Operates Entirely at the Edge of the Network

Print Friendly, PDF & Email

Predixion_logoPredixion Software, a developer of edge analytics software, announced the release of Predixion RIOT, real-time visual edge analytics software. Among other market-leading capabilities, RIOT was designed to work with the latest Intel® IoT Gateway Technology featuring Wind River Intelligent Device Platform XT 3.1.

Predixion’s RIOT software solves the challenge increasingly visible in Internet of Things (IoT) installations – where large volumes of data never get used due to the high-cost and latency in using central, cloud-based analytics. This challenge will only increase as more devices are connected. Gartner, Inc. forecasts that 6.4 billion connected things will be in use worldwide in 2016, up 30 percent from 2015, and will reach 20.8 billion by 2020. In 2016, 5.5 million new things will get connected every day[1]. According to Roy Schulte and Rita Sallam at Gartner, “Analytics are essential to the success of IoT systems. They are arguably the main point of the IoT as they support the decision-making process in operations that are created in business transformation and digital business programs.[2]” The volume and velocity of data generated from the IoT will mandate the need for new architectures and drive the demand for applying analytics closer to the edge.

Predixion RIOT™ has been built from the ground up with a new architecture that enables users to gain real-time insights on IoT data when and where it is most impactful – whether on the device itself running an RToS, on the IoT gateway based on Intel IoT Gateway Technology, or with a consolidated cloud-based view of all devices in an organization. The Predixion RIOT family of products addresses the needs at all layers for IoT analytics. With intelligence at the edge enabled by Intel IoT Gateway Technology, the combined solution helps businesses achieve near real-time analysis and tighter, more efficient process controls, while reducing data transmission and storage costs.

IoT promises significant advances for users, but the fact that, even now, only 10 percent of IoT data even makes it to the cloud[3], proves that current analytics solutions are too reliant on central processing to be effective. To be able to deal with the dramatic growth in data volumes, analytics performed at the edge becomes mandatory,” said Simon Arkell, CEO, Predixion Software. “RIOT changes the game, delivering the industry’s fastest and most efficient real-time, IoT visual edge analytics solution. RIOT enables users in markets such as manufacturing, energy, and healthcare to gain faster time to insight via the delivery of real-time visual edge analytics with full integration to the orchestration capabilities of the cloud.”

Predixion RIOT is the industry’s first visual edge analytics software that truly lives and operates at the edge of the network. It delivers the following new capabilities for users:

  • Ease of Deployment: Out of the box, organizations can dynamically deploy advanced analytics on-device, on-gateway, or in-the-cloud;
  • Continuous Actions: RIOT supports connected, partially-connected and disconnected environments so decisions can be made in real-time without reliance on network connectivity;
  • Visual Analytics Trigger Real Time Actions: RIOT allows organizations to conduct predictive maintenance and predictive operations management, with workflows triggered by the output of the analytics.

At Intel, we see IoT growing into nearly every part of our lives and connecting the ‘things’ that were never connected before. Yet there are barriers slowing IoT adoption and Intel is working to provide solutions that tackle these challenges,” said Jonathan Ballon, General Manager, IoT Solutions, Intel. “One of the key challenges is delivering real-time, insightful and secure data analytics. Predixion’s analytics engine supports Intel’s IoT strategy by enabling these analytics from the EDGE to the cloud.”

Predixion RIOT:

  • Predixion RIOTNano is a complete, self-contained, small foot-print real-time visual analytics engine which runs on a single device and provides immediate visual analytics and insights from device sensors. RIOT Nano provides on-device visual analytics or stream analytics to the gateway or cloud so a user can easily have visual insights from all connected devices.
  • PredixionRIOT One is a complete, self-contained real-time visual analytics platform which runs on the gateway, connects to thousands of devices and provides “instant-on” visual analytics with minimal configuration and setup. RIOT One provides user-driven visual analytics so a business user can gain instant insights into device operations, health and performance of the device, and easily apply new analytics to gain immediate insight for rapid decisions and actions. RIOT One runs at the edge, which is closer to the data, and can provide visual analytics directly from the gateway without requiring any cloud presence, thus providing quicker insight, and radically reducing the configuration and processing complexity compared to using a cloud-only analytics approach.
  • Predixion RIOT Enterprise is a cloud-based edge analytics platform and provides aggregated real-time visual analytics across all connected devices. Real-time analytics are streamed from gateways and devices to RIOT Enterprise in the cloud to provide a centralized view of the performance and insight of all connected devices.

Solving IoT Challenges

Connected devices and machines are an integral part of operations in just about every industry today. Whether in a factory, mine, fleet, hospital or home, these devices are all producing a massive amount of data, which is projected to reach 1,600 exabytes by the year 2020[1]. As the volume and velocity of IoT data dramatically increases, it is essential to move real-time analytics closer to the data itself—at the edge. Predixion RIOT enables users to securely capture and harness the power of IoT data, delivering faster ways to realize the value of sensor information, and transform that into predictive maintenance insights on the health of the devices and their environment, that users can act upon instantly to get ahead of unplanned downtime.

[1] Gartner IoT press release, Nov 10 2015

[2] Gartner, Three Best Practices for Internet of Things Analytics, Roy Schulte, Rita L. Sallam, 23 October 2015

[3] ABI Research ‘Competitive Edge from Edge Intelligence – IoT Analytics Today and 2020’, May 1, 2015

Sign up for the free insideBIGDATA newsletter.

Speak Your Mind