Real-time applications and simulations that track thousands of data sources, such as vehicles in a fleet or sensors on a security perimeter, need to be able to immediately identify hot spots and determine whether a pattern exists. This enables fast, strategic responses to emerging threats and optimal use of scarce resources. Combining real-time analytics performed by digital twins with continuous data aggregation and visualization gives operational managers a powerful tool for identifying areas of concern and boosting situational awareness.
Consider an application that tracks hospital supplies nationwide during a crisis like the recent COVID pandemic. This application can use real-time digital twins to keep track of critical supplies, such as ventilators, at each hospital within a nationwide network. Data aggregation can then pinpoint the regions with the most pressing needs and indicate the nearest locations where supplies are available.
Digital twins combine incoming telemetry with contextual information to create highly curated results that offer deeper insights than raw telemetry for visualizing trends.
ScaleOut’s in-memory computing platform uses data-parallel computing techniques that harness multiple servers to quickly aggregate and query digital twin data.
Both widgets and geospatial queries refresh their results every few seconds to immediately visualize emerging trends and enable fast action by operational managers.
The ScaleOut Digital Twin Streaming Service’s UI enables fast, easy creation of real-time, aggregate analytics that combine the state of all digital twins of a given type and provide immediate, graphical feedback. Each analytics “widget” aggregates a measurement of interest using a grouping specified by a property, such as a geographical region. It displays the results as a bar, pie, or line chart and updates every few seconds.
In addition, the streaming service’s UI offers powerful query capabilities on the dynamic state of digital twins, whether running in a live application or in simulation. Queries instantly identify which data sources have properties of interest, and they can plot their results on a geospatial map that updates every few seconds. For example, in the above scenario, queries can determine which specific hospitals have the largest shortfall in supplies and where supplies are located.