Japanese electrical equipment manufacturing company Mitsubishi Electric has developed a sensor database that stores, searches and aggregates sensor data for IoT.
According to a release, the database enables sensor data to be used for urban infrastructure maintenance, for example, maintaining roads, railways, monitoring factories and managing energy use in buildings and homes.
The database has the capacity to process up to 100 trillion data items, such as three-dimensional measurements by laser sensors on/around roads totaling 200,000 kilometers in length. It can also process sensor data accumulated in a factory over a three-year period using 100,000 sensors that take measurements every 100 milliseconds.
Expedited data processing
Mitsubishi Electric claims that its sensor database reduces storage space, load time, and search and aggregation time each to a range of just one-tenth to one-thousandth of current levels.
According to the company, these outcomes were achieved through - data compression, to reduce input/output data traffic by selecting compression patterns from more than 700 combinations. Secondly, the company was able to reduce processing times by arranging compressed data in storage blocks to reduce input/output time, and thirdly, through by processing data in cache memory where possible to enhance parallel.
The Tokyo-based company states that data-processing performance can be improved through hardware enhancement, like parallel or distributed processing using many servers, in-memory processing using large amounts of memory, or fast storage devices using flash memory. It notes however, that these methods require expensive hardware, whereas its sensor database uses a single server with only one or two CPUs and 4GB of main memory.
Mitsubishi also states that the sensor database allows for incremental expansion of servers as the amount of data stored searched and aggregated grows. It adds that this this is a critical feature as sensor data generally increases with long-term use and that many trial systems begin with small amounts of data for testing, before a full scale operation is rolled out.