ScaleOut’s founder and CEO, Dr. William Bain, will give a talk on real-time digital twins at the upcoming In-Memory Computing Summit on Nov 13-14 at the Hyatt Regency San Francisco Airport. This talk will explain how in-memory computing enables an exciting new software technology, called “real-time digital twins,” to perform streaming analytics at scale. This technology makes it possible to simultaneously track and analyze telemetry from more than 1M data sources. The use of real-time digital twins can dramatically boost situational awareness in applications which need to quickly pinpoint and react to critical events among a large number of data sources. Applications include disaster recovery, cyber-security, healthcare tracking, financial services, industrial IoT, and more.
The talk will describe new APIs for building software-based, real-time digital twin models that are then deployed to run on either an in-memory data grid or in the cloud. These APIs give developers a compelling new software architecture for building stream-processing applications that run on a scalable, in-memory computing platform. The use of digital twins simplifies application development while enabling transparent scalability to track very large numbers of data sources. Running these models on an in-memory computing platform also ensures that streaming events can be processed with low latency and built-in high availability.
Perhaps the most important benefit of this new approach to stream processing is that it enables real-time aggregate analytics that can spot important patterns and trends within seconds and then provide an immediate and effective response in rapidly evolving situations. Instead of forwarding key data about the state of a large, dynamic system to a data lake for later analysis, now this data can be continuously analyzed in real time, dramatically shortening the time to action. The real-time digital twins collect, analyze, and filter key telemetry from numerous data sources, and continuous, aggregate analytics sift through this data to identify actionable trends.
For example, in a cyber security application, real-time digital twins can track telemetry from a large population of nodes within a power grid or other complex deployment and detect potential emerging threats. At the same time, aggregate analytics can determine the scope of an attack or outage within seconds and assist personnel in developing a response and directing resources to the highest priority threats. The talk will demonstrate a simulation of this use case to illustrate the combined simplicity and power of the real-time digital twin approach.
