Knowledge-based decision support systems and method for process lifecycle automation

A knowledge-based decision support method providing process lifecycle automation, the method comprising:

• registering, via a hardware processor, one or more software applications and data sources, wherein the registered one or more software applications comprise at least one external software application, wherein the external software application is a predictive analytical model;
• defining, via the hardware processor, one or more processes to be implemented by executing the registered one or more software applications, wherein the definition of the one or more processes span across the one or more software applications;
• orchestrating, via the hardware processor, execution of the registered one or more software applications for implementing the one or more processes, wherein the orchestration facilitates users utilizing the software application or the processes to create and run instances of other processes, wherein the processes includes ad hoc processes for experimentation;
• monitoring, via the hardware processor, system performance based on the execution of the registered one or more software applications by:
• facilitating acquisition and analysis of a process execution data using machine learning techniques;
• identifying performance issues of the one or more processes;
• and
• triggering notifications to one or more users indicating the performance issues, wherein the notifications are sent to at least one of a corresponding software application and the users utilizing the software application for implementing remedial measures;
• generating, via the hardware processor, analytics data related to the monitored system performance;
• updating, via the hardware processor, a database with historical data using the generated analytics data;
• generating, via the hardware processor, an analytical report by analyzing, using a machine learning technique, the historical data stored in the database as well as the generated analytics data;
• generating automatically, a user interface based on user inputs, wherein the user inputs comprise at least one of a layout and a specification provided by a user;
• modifying, via the hardware processor, execution of the registered one or more software applications based on the generated analytical report;
• and
• displaying, via the hardware processor, data from the generated analytical report using the automatically generated user interface.

A non-transitory computer-readable medium storing instructions, which, when executed by a hardware processor, cause the hardware processor to perform acts comprising:

• registering, via a hardware processor, one or more software applications and data sources, wherein the registered one or more software applications comprise at least one external software application, wherein the external software application is a predictive analytical model;
• defining, via the hardware processor, one or more processes to be implemented by executing the registered one or more software applications, wherein the definition of the one or more processes span across the one or more software applications;
• orchestrating, via the hardware processor, execution of the registered one or more software applications for implementing the one or more processes, wherein the orchestration facilitates users utilizing the software application or the processes to create and run instances of other processes, wherein the processes includes ad hoc processes for experimentation;
• monitoring, via the hardware processor, system performance based on the execution of the registered one or more software applications by:
• facilitating acquisition and analysis of a process execution data using machine learning techniques;
• identifying performance issues of the one or more processes;
• and
• triggering notifications to one or more users indicating the performance issues, wherein the notifications are sent to at least one of a corresponding software application and the users utilizing the software application for implementing remedial measures;
• generating, via the hardware processor, analytics data related to the monitored system performance;
• updating, via the hardware processor, a database with historical data using the generated analytics data;
• generating, via the hardware processor, an analytical report by analyzing, using a machine learning technique, the historical data stored in the database as well as the generated analytics data;
• generating automatically, a user interface based on user inputs, wherein the user inputs comprise at least one of a layout and a specification provided by a user;
• modifying, via the hardware processor, execution of the registered one or more software applications based on the generated analytical report;
• and
• displaying, via the hardware processor, data from the generated analytical report using the automatically generated user interface.