Compression algorithms have been around for decades. They are a useful tool for data reduction in certain situations. However, these algorithms come with considerable limitations when applied to IoT applications and Cloud storage.
Compression algorithm limitations include: NOTE: AtomBeam overcomes ALL these Compression Limitations
- High Computational Latency: complex compression algorithms take time to process (and often have to be read multiple times before compressing). Compression simply adds too much latency for time-sensitive use-cases where data must be processed instantaneously. Examples include health monitoring, factory automation, autonomous cars, and others.
- Inability to Handle Short IoT Data Bursts: IoT devices, sensors, actuators and monitors often transmit short bursts of information, relatively infrequently. Because compression needs to process over 8000 bits before its algorithms can be applied, so it’s not useful in many bursty IoT applications.
- Extreme Error Sensitivity: many IoT and remote applications use wireless & satellite networks. As we all know, these networks are prone to network errors and transmission interruptions. Compression is very sensitive to these errors, and must sometimes re-transmit entire files, and in other cases compressed files are rendered worthless if they contain errors.
- Can’t Search Compressed Files: once files are stored in compressed formats, they must be completely de-compressed before searching and finding information — a time-consuming, arduous process.
- Lossy Algorithms: some compression algorithms do not produce 100% accurate data (called lossy compression).
- Dedicated Data Type Algorithms: different compression algorithms are necessary to compress different types of data. For heterogeneous data transfer and storage, this is a severe limitation.
AtomBeam 1) has low computational latency, 2) handles short IoT data bursts, 3) is error tolerant, 4) can search compacted files, 5) is 100% accurate, and 6) supports all data types.