The Internet of Things, Big Data, and Artificial Intelligence mark the convergence of information and communication technologies that has ushered in the arrival of the fourth industrial revolution. Thanks to Industry 4.0, hyper connectivity and hyper intelligence are rapidly progressing throughout the home and business.
In order to support the requirements for this new era, new technologies such as automatic setting of objects and autonomous control have emerged to support operational optimization. The IoT data that is generated from the various sensor devices used in both home-based and industrial applications has special characteristics and constant data formats, such as timestamp, ID, and measurement value.
As usage increases in household appliances, buildings and manufacturing facilities, the growing number of sensor devices and the increase in periodically generated data that follows requires new specialized solutions. In order to monitor IoT data in real time and gain its insights, one area in which these new solutions are showing up is the emerging Operational Intelligence sector.
Operational intelligence (OI) is categorically defined as the streaming data and associated business analytics used to deliver increased visibility and insight into business operations. OI solutions allow the analysis of real-time data feeds and event data to deliver operational instructions from analytic results.
In other words, OI lets businesses analyze operational data in real-time and quickly take advantage of organizational insights through automation or manual actions.The core of Operational Intelligence in IoT is to collect, store and analyze IoT data in real time. A good OI solution should visualize this operational data in real time and securely provide impactful business insights. Click To Tweet
Key components for OI
1) Real-Time Monitoring And Visualized Data
At its core, true OI relies on the ability to monitor real-time data trends in order to report analytics. To do this, OI solutions require flexible, rapid chart generation that can keep up with the massive volume of real-time input data that is quickly generated in an IoT environment.
In addition, your OI solution needs to be capable of producing comprehensive, colorful, and diverse visuals that can display complex data. Implementing the latest web-based technologies, such as HTML5, also allows data charts to be more accessible, and available through not only a desktop PC, but also on tablets or mobile devices.
2) Complex Event Processing (CEP)
Complex Event Processing (CEP) is an in-memory based processing technology that processes data in memory before it is input into storage. By doing this, it can handle data efficiently and quickly, even at rates of hundreds of thousands of data inputs per second. Due to it’s efficient and timely analysis, CEP technology can provide businesses with valuable real time monitoring and early warning capabilities.
Traditional data processing methods store and query IoT data in a relational database management system (RDBMS), without the use of CEP technology. As such, RDBMS is not suitable for processing large amounts of data in real time, it isn’t an appropriate solution for effective OI.
3) Alarms & Notifications
Creating automatic alarms and notifications attached to select data inputs can provide impactful, timely operational insights. This can be done by creating Upper Control Limits (UCL) and Lower Control Limits (LCL) for different input data, and then setting up notifications via SMS or Email for when these limits are met.
Alarms & notifications allow administrators to take appropriate action at the right time, more quickly mitigating otherwise unnoticed issues.
Advanced systems are able to respond autonomously by algorithm, without administrator intervention. These algorithms can even be continuously improved through machine learning based on historical data, further increasing OI effectiveness.
Machbase, Time Series DBMS for OI
Machbase is a time series DBMS, optimized fast data input and retrieval. It is suitable for system configuration for OI.
1) Consume less resources
Machbase uses less resources in CPU, MEMORY and DISK. Therefore, it can be used as a database for Edge Analytics since it is integrated and operates at the port of gateway devices including Samsung ARTIK. In a could or central server environment, even if you configure a cluster with normal server devices, large amounts of data still can be processed in real time.
2) Fast data entry
When data is input from multiple sensors, the performance of data input becomes extremely important. Machbase can handle around 50,000 events per second in gateway devices. A single server can handle hundreds of thousands of records per second with Machbase. In a cluster environment, 10 million records can be collected and stored per second. Therefore, even if a large amount of data is generated, Machbase can process in real time.
3) Search for time series data
In OT, the recent data is more important than the past. Also, most queries have time range search conditions. Machbase stores data by time-based partitioning and provides convenient syntax for time series data search, enabling fast data retrieval.
4) Data visualization
Machbase provides its own web-based chart and dashboard functions. It can create dashboards in conjunction with Grafana, an open source chart solution. In addition, it supports ODBC, JDBC and RESTful API, so it can easily generate charts and analyze data in real time by integrating with R, Tableau, and BI solutions.
Today’s business needs are constantly and rapidly changing in the Industry 4.0 era. In order to respond quickly to these changes, system efficiency and intelligence are absolutely essential. Real-time monitoring and analysis of the high-volume of sensor data generated in IoT environments allow companies to make rapid decisions.
Implementing modern systems for OI is no longer optional, but critical in order for businesses to remain competitive in today’s markets. Machbase is an optimized OI solution and the most suitable choice for IoT data.