AtomBeam can compact extremely small messages—previously impossible to reduce.
Data compaction is a process that uses Machine Learning to reduce the size of messages by sending tiny codewords that represent patterns in data—rather than actually sending any data.
Compaction works on individual messages, including very small messages—IoT and machine data—messages as small as 8 bytes.
Compaction works best on low-entropy IoT or machine data.
It uses machine learning on a representative dataset and identifies patterns at the bit level across thousands of messages.
When a pattern is identified it is assigned a Codeword and that Codeword is placed into a Codebook.
When complete, the Codebook is placed in the source (such as the sensor), and the destination (such as the cloud.)
Source and destination now communicate solely in Codewords.
Only Codewords are transmitted.
AtomBeam Is Lossless: We Do Not Lose Even One Bit (Literally)
The data moves as fast as it is generated:
There are IoT security technologies, but all add latency, increase file size, and require additional cost for the software/hardware.
It’s important to understand the differences between Compaction and Compression. Click this link to see a comparison between Compaction VS Compression.