OCTO TIME-SERIES PROCESSING
OCTO MESH Time-Series Processing Service ensures the preparation and transformation of data that is recorded in chronological order: Time-series, alarms, events, etc. This enables you, for example, to recognize trends, capture seasonal patterns and plan your future by using historical data to forecast future values.
The OCTO TIME-SERIES service guarantees seamless recording thanks to rolling system and database updates. Included redundancy concepts and automated backups based on CrateCB technology. High scalability and performance thanks to clustering options for the database infrastructure.
High-performance data processing thanks to the integration of CrateDB, which enables massive write performance (e.g. more than 40,000 inserts per second per node on standard hardware). In addition, the database can deliver the fastest query performance in milliseconds, even when write operations are in progress. Without having to compromise on standards: The query or processing is carried out using standard SQL syntax, which is supported by many applications out-of-the-box.
Typical applications for time series data are, for example:
- Industrial IoT: Integrate industrial systems and devices with the Internet of Things (IoT) to collect, transmit and utilize data. Process, store and analyze large amounts of real-time data from multiple sources with time series data processing. Improve the performance, efficiency and safety of your industrial processes.
- Prozesssteuerung und -überwachung: Control and optimize your industrial processes with the help of control and feedback mechanisms. Use time series data processing to measure, monitor and adjust process variables such as temperature, pressure, flow, etc. Increase product quality while reducing energy consumption and operating costs, for example.
- Anomaly detection: This is the identification of unusual or deviating patterns or events in the data. Time series data processing is used here to detect and explain anomalies such as outliers, trends, cycles or seasonalities. This can help to detect and rectify errors, faults or attacks in industrial systems at an early stage.