Distributed big data in a process control system

A method of supporting distributed big data using a device communicatively coupled to a communications network of a process plant and operating to control a process in real-time in the process plant, the method comprising:

• collecting data at the device,
• the data including at least one of:
• (i) data that is generated by the device, (ii) data that is created by the device, or (iii) data that is received at the device,
• the data resulting from the control of the process in real-time, and
• a type of the device is included in a set of device types, the set of device types including a field device, a controller, and an input/output (I/O) device;
• storing, in an embedded big data apparatus of the device, the collected data;
• performing, by the embedded big data apparatus of the device, a learning analysis on at least a portion of the stored data;
• generating learned knowledge indicative of a result of the learning analysis;
• and
• modifying, based on the learned knowledge, an operation of the device to control the process in real-time.

A system for supporting distributed big data in a process plant comprising:

• a communications network having a plurality of nodes, at least one of which is a process control device operating, in real-time, to control a process executing in the process plant, and each of the plurality of nodes is configured to:
• collect data generated in real-time resulting from control of the process executing in the process plant;
• locally store the collected data at a respective embedded big data apparatus included in the each of the plurality of nodes;
• and
• perform, by the respective embedded big data apparatus included in the each of the plurality of nodes, a respective learning analysis on at least a portion of the locally stored data, and
• wherein a first node included in the plurality of nodes is further configured to cause learned knowledge indicative of a result of a performance of the respective learning analysis to be transmitted to a second node included in the plurality of nodes for use in the respective learning analysis of the second node or in another respective learning analysis of the second node.