This advanced functionality, says the company, is especially relevant in edge computing applications - such as smart cities, wearables and transportation - that exist to manage constantly changing time and location variables. The new capabilities – primarily geospatial, temporal, and time-series data – can be easily combined with operational and customer analytics, enabling users to operationalize enhanced IoT analytics use cases.
"We are on the brink of a massive explosion of IoT applications and use cases powered by advanced analytics," says Tim Henry, Senior Vice President, Strategic Offering Management at Teradata. "Devices at the edge, like connected cars, fleets, planes, traffic lights, roads, wearables and so much more, will become smarter and more valuable as new analytic insight is pushed out to them.
"Teradata, with the industry's first 4D Analytics capability," says Henry, "is primed to lead this revolution of smarter edge computing, driving broad improvements that range from reduced traffic and greater energy efficiency, to increased transportation safety."
The Teradata Analytics Platform, says the company, makes edge computing smarter and enables business impact analysis, bringing deeper insights to traditional sensor data analysis. The addition of 4D Analytics capability enhances IoT analytics by using insights based on both the time and "space" of a device - such as cars and wearables.
Combining time-series (a series of data points collected at set intervals that shows activity and changes over time), temporal (for storing data related to relevant time periods) and geospatial data (that is associated with a device's location), it provides contextual analytics based on when and where.
According to the company, its 4D Analytics capability can power a variety of enhanced applications:
- Analyzing patterns of trains, subways, cabs, automobiles, traffic lights, restaurant traffic and general citizen movement, resulting in new insights back at the edge as new logic and rules to make smart cities, smarter
- Studying sensor data from a vehicle fleet, such as travel times and routes, to optimize