October 11, 2017 -
Tellmeplus, the specialist of Artificial Intelligence applied to Big Data, and QuasarDB, provider of a software storage technology optimized for real-time data analysis, announced today a strategic technology partnership. Under the terms of the agreement, Tellmeplus will be embedding the QuasarDB time series database into its Predictive Objects platform, enabling the deployment of next generation predictive models into any connected object or system.
QuasarDB is a high-performance, distributed, column-oriented database with native time series support. The database can reliably ingest millions of points per second and aggregate billions of lines per second, making sure it can handle even the most extreme time series use cases while enabling interactive work on data, regardless of the amounts stored.
With transparent scalability from very large and dense time series to smaller ones, QuasarDB can be deployed seamlessly on any platform, from memory- and CPU- constrained connected objects to edge computing gateways or cloud servers.
“Embedding a scalable time serie oriented data storage in our predictive analytics solution is key to an efficient deployment of predictive models on any type of object or platform,” indicated Jean-Michel Cambot, founder and chief strategist at Tellmeplus. “Transfers, computations of aggregations in QuasarDB are so fast and require so little memory that Predictive Objects will be able to compute their own sequences and expose them to the embedded model with the same sequencing power, regardless of where they are deployed.”
Edouard Alligand, founder and CEO of QuasarDB, added: “Tellmeplus’ Predictive Objects platform is a game-changer in artificial intelligence, with its ability to deploy and run predictive models inside the objects, where the data is produced and where insight is needed the most. We are very excited about QuasarDB enabling them to be even more efficient.”
The first version of Predictive Objects that natively embeds QuasarDB is expected to be released in November 2017.
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