Automatic discovery of automated digital systems through link salience

A system comprising at least one computing device comprising at least one processor, a memory, and instructions executable by the at least one processor to create an application comprising:

• a) a software module detecting activity in a plurality of data sources associated with an automation environment by automatically issuing a set of commands to cycle a current data source in the plurality of data sources, wherein the set of commands to cycle comprises a command to power down the data source, a command to power up the data source, a command to reboot the data source, a command to change one or more parameters or configurations of the data source, or a combination thereof;
• b) a software module determining correlation in the detected activity between a non-cycled data source and the current data source by monitoring the automation environment for events or state changes triggered by cycling the current data source, wherein the events or state changes are indicative of the correlation between the non-cycled data source and the current data source;
• c) a software module storing records of determined correlation in the detected activity over time in a data storage system, wherein the records of determined correlation are associated, respectively, with timestamps;
• d) a software module applying a link salience algorithm to the stored records of determined correlation in the detected activity to determine a salience property, wherein the stored records of determined correlation comprise one or more data changes associated with the records of determined correlations over time;
• e) a software module analyzing the salience property to determine a correlation count during a pre-determined time period based at least in part on the timestamps associated with the records of determined correlation, wherein the correlation count is indicative of the degree of correlation between two or more of the data sources;
• f) a software module analyzing the stored records of determined correlation to extract directionality information associated with the correlations, wherein the directionality information is indicative of a causal relationship between data sources;
• and
• g) a software module identifying one or more subsystems in the automation environment based on the salience property, the directionality information, and the correlation count.

A computer-implemented method comprising:

• a) detecting activity in a plurality of data sources associated with an automation environment by automatically issuing a set of commands to cycle a current data source in the plurality of data sources, wherein the set of commands to cycle comprises a command to power down the data source, a command to power up the data source, a command to reboot the data source, a command to change one or more parameters or configurations of the data source, or a combination thereof,
• b) determining correlation in the detected activity between a non-cycled data source and the current data source by monitoring the automation environment for events or state changes triggered by cycling the current data source, wherein the events or state changes are indicative of the correlation between the non-cycled data source and the current data source;
• c) storing records of determined correlation in the detected activity over time in a data storage system, wherein the records of determined correlation are associated, respectively, with timestamps;
• d) applying a statistical or machine learning algorithm to the stored records of determined correlation in the detected activity to determine a property, wherein the stored records of determined correlation comprise one or more data changes associated with the records of determined correlations over time;
• e) analyzing the property to determine a correlation count during a pre-determined time period based at least in part on the timestamps associated with the records of determined correlation, wherein the correlation count is indicative of the degree of correlation between two or more of the data sources;
• f) analyzing the stored records of determined correlation to extract directionality information associated with the correlations, wherein the directionality information is indicative of a causal relationship between data sources;
• and
• g) identifying one or more subsystems in the automation environment based on the property, the directionality information, and correlation count.

A system comprising at least one computing device comprising at least one processor, a memory, and instructions executable by the at least one processor to create an application comprising:

• a) a software module detecting activity in a plurality of data sources associated with an automation environment by automatically issuing a set of commands to cycle a current data source in the plurality of data sources, wherein the set of commands to cycle comprises a command to power down the data source, a command to power up the data source, a command to reboot the data source, a command to change one or more parameters or configurations of the data source, or a combination thereof;
• b) a software module determining correlation in the detected activity between a non-cycled data source and the current data source by monitoring the automation environment for events or state changes triggered by cycling the current data source, wherein the events or state changes are indicative of the correlation between the non-cycled data source and the current data source;
• c) a software module storing records of determined correlation in the detected activity over time in a data storage system, wherein the records of determined correlation are associated, respectively, with timestamps;
• d) a software module applying a statistical or machine learning algorithm to the stored records of determined correlation in the detected activity to determine a property, wherein the stored records of determined correlation comprise one or more data changes associated with the records of determined correlations over time;
• e) a software module analyzing the property to determine a correlation count during a pre-determined time period based at least in part on the timestamps associated with the records of determined correlation, wherein the correlation count is indicative of the degree of correlation between two or more of the data sources;
• f) a software module analyzing the stored records of determined correlation to extract directionality information associated with the correlations, wherein the directionality information is indicative of a causal relationship between data sources;
• and
• g) a software module identifying one or more subsystems in the automation environment based on the salience property, the directionality information, and the correlation count.