One embodiment provides for generating a cognitive executable process graph including obtaining, by a processor, a hybrid process knowledge graph generated based process fragments and a set of actionable statements and business constraints. The hybrid process knowledge graph including different node types. The hybrid knowledge graph is traversed from a root of a process through each task in the hybrid process knowledge graph to obtain an action and metadata for each task node. Based on the action and metadata, at least one statement in an equivalent executable code block is created to represent the action. A cognitive executable process graph is generated based on at least one executable code block.

In an example embodiment, a data model infrastructure is implemented as a service rather than as an always-running server. Specifically, one of the technical issues with past implementations is that the data models are deployed onto a server that is intended to be “always running”, even if there are no requests to the server. This utilizes memory and processing power. While it may be useful to have an always running server for commonly used applications, for applications that are infrequently used (e.g., 10 times a month), it can mean that memory and processing power is wasted. Thus, by implementing the data model infrastructure as a service rather than an always-running server, the service can be launched only when actually needed, saving both memory and processing power.

In an example embodiment, a data model infrastructure is implemented as a service rather than as an always-running server. Specifically, one of the technical issues with past implementations is that the data models are deployed onto a server that is intended to be “always running”, even if there are no requests to the server. This utilizes memory and processing power. While it may be useful to have an always running server for commonly used applications, for applications that are infrequently used (e.g., 10 times a month), it can mean that memory and processing power is wasted. Thus, by implementing the data model infrastructure as a service rather than an always-running server, the service can be launched only when actually needed, saving both memory and processing power.

An information processing method includes: selecting M blocks as M selected blocks from among N blocks for driving at least one of an actuator or a heater included in an apparatus, in accordance with an input operation performed by an operator; generating an application, by setting the order of the M selected blocks in accordance with an input operation performed by the operator; consulting a rule that prohibits two or more given blocks from being executed in a given order, and when information included in the application that indicates the order of the M selected blocks correlates with the given order, modifying the application by changing the order in which each of the M selected blocks is executed; and outputting the modified application.

Current approaches to integrating industrial ecosystems, for instance integrating automation functions across different vendors, lack efficiencies and capabilities. For example, system integrators are often required to develop special software that functions as a proxy or adaptor between different systems. In such cases, the proxy or adaptor is often specific to a particular set of equipment or vendors, and which can limit reusability, among other technical drawbacks. Embodiments described herein overcome one or more of the described-herein shortcomings or technical problems by providing methods, systems, and apparatuses for automatically generating connecters that enable interoperability between different ecosystems in automated industrial systems, and that define semantics that are specific to a given ecosystem. Further, such connectors can be re-used by the given ecosystem.

Computer processes are provided which can be executed without business or commercial context, independent of the kind of data or other content associated with the computer processes. In one embodiment, a computer process can be broken down into functional units, and the metadata associated with the functional units can be extracted. Each functional unit can then be represented by an interface and also coded with computer-readable instructions to use one or more configuration sets which have been defined by the metadata. The computer process can then be implemented by programming the functional units to execute based on a configuration set determined by predefined operating parameters. Artificially intelligent algorithms may be used to analyze and self-configure the processing flow or business rules aspects of different events associated with the computer process.

An industrial integrated development environment (IDE) supports collaborative tools that allow multiple designers and programmers to remotely submit design input to the same automation system project in parallel while maintaining project consistency. The industrial IDE also permits localized development of system projects, and provides an infrastructure for intelligently brokering between conflicting edits submitted to common portions of the system project.

An industrial integrated development environment (IDE) supports collaborative tools that allow multiple designers and programmers to remotely submit design input to the same automation system project in parallel while maintaining project consistency. The industrial IDE also permits localized development of system projects, and provides an infrastructure for intelligently brokering between conflicting edits submitted to common portions of the system project.

The present invention extends to methods, systems, and computer program products for deriving unified insights ad logs from DevOps CI/CD tools and pipeline data. In general, a data transformer facilitates data normalization and serialization converting raw data across multiple DevOps tools and stores the data into a Data Lake in accordance with a customized schema. A continuous orchestrator sequences, aggregates and contextualizes the logs, providing an intuitive way of troubleshooting issues across a DevOps environment, historical data for compliance and audit purposes, and a build manifest for root cause analysis. The continuous orchestrator also processes the logs and leverages a KPI framework, providing intelligent dashboards across 90+ KPI's and a plurality of different dimensions (Planning, Development/pipelines, security, quality, operations, productivity and source code) to help customers make smart decisions and do more with less.

The present invention extends to methods, systems, and computer program products for deriving unified insights ad logs from DevOps CI/CD tools and pipeline data. In general, a data transformer facilitates data normalization and serialization converting raw data across multiple DevOps tools and stores the data into a Data Lake in accordance with a customized schema. A continuous orchestrator sequences, aggregates and contextualizes the logs, providing an intuitive way of troubleshooting issues across a DevOps environment, historical data for compliance and audit purposes, and a build manifest for root cause analysis. The continuous orchestrator also processes the logs and leverages a KPI framework, providing intelligent dashboards across 90+ KPI's and a plurality of different dimensions (Planning, Development/pipelines, security, quality, operations, productivity and source code) to help customers make smart decisions and do more with less.