Processing the data for a database search, analytics, or AI functionality requires a server-class processor that has its own memory resources and the ability to run applications in a higher-level ...

Their ability to host an in-memory model of a real-world system, track changes as they occur, and analyze the model in real time both simplifies this task and delivers the performance that’s needed.

Samsung extends it Processing-in-Memory (PIM) technology from HBM to DIMMS and LPDDR5. Pliops computational storage and flash management device will provide lower infrastructure costs for big data ...

Hazelcast Inc. today introduced a new in-memory data processing platform that it says will enable companies to analyze historical and real-time information at the same time. San Mateo, California ...

With real-time analytics, data processing is revolutionized as data can be used and instantly analyzed as soon as it is generated. ... In-Memory vs. Traditional Database.

Business analytics based on in-memory computing will continue to gain traction even as companies try to make sense--in real-time--of the ever-increasing volume of data generated, noted an analyst ...

Open-source big-data platform Hadoop excels at batch-mode processing at scale but it was never designed for real-time analytics. ScaleOut Software has middleware that it thinks addresses the issue.

Real-time data processing is the handling of data as it arrives, so it is available for use almost as immediately as it is created and collected. ... Server memory (DRAM) is costly and uses a lot of ...

If external memory had no latency, you could process video frames from external memory. Unfortunately, this situation is not the case. Modern embedded-processor architectures that can perform dual ...

Stream processing is designed to analyze and act on real-time streaming data, using “continuous queries” (i.e. SQL-type queries that operate over time and buffer windows).