Abstract: Techniques that facilitate feedback loop learning between artificial intelligence systems are provided. In one example, a system includes a monitoring component and a machine learning component. The monitoring component identifies a data pattern associated with data for an artificial intelligence system. The machine learning component compares the data pattern to historical data patterns for the artificial intelligence system to facilitate modification of at least a component of the artificial intelligence system and/or one or more dependent systems of the artificial intelligence system.

Abstract: Several aspects are provided for dynamically updating an alert-management system that uses a master ruleset to match alerts in a data processing system with automata for handling the alerts. A method comprises training a machine learning model to correlate the alerts with the automata using a training dataset comprising alerts which were successfully handled by the automata. The machine learning model is then applied to correlate unmatched alerts with the automata, wherein the unmatched alerts were not matched to the automata by the master ruleset. The method further comprises analyzing operation of the machine learning model in relation to correlation of the unmatched alerts to define a new ruleset for matching the unmatched alerts with the automata and outputting the new ruleset for auditing of each rule in the new ruleset. In response to approval of an audited rule, the audited rule is added to the master ruleset.

Abstract: The invention relates to a computer-implemented method for processing events. The method provides a database comprising original event objects stored in association with canonical event objects. The method executes a learning algorithm on the associated original and canonical event objects for generating a trained ML program adapted to transform an original event object of any one of the one or more original data formats into a canonical event object having the canonical data format and uses the trained machine learning program for automatically transforming original event objects generated by an active IT-monitoring system into canonical event objects processable by an event handling system.

Abstract: Techniques that facilitate feedback loop learning between artificial intelligence systems are provided. In one example, a system includes a monitoring component and a machine learning component. The monitoring component identifies a data pattern associated with data for an artificial intelligence system. The machine learning component compares the data pattern to historical data patterns for the artificial intelligence system to facilitate modification of at least a component of the artificial intelligence system and/or one or more dependent systems of the artificial intelligence system.

Abstract: A new technique to analyze the control-flow, i.e., the workflow graph of a business process model, which is called symbolic execution, is provided. Acyclic workflow graphs that may contain inclusive OR-gateways are considered; a symbolic execution for them is defined, which runs in quadratic time. In particular, this symbolic execution essentially comprises labeling edges of nodes of the graph such that a label assigned to a first edge comprises a set of one or more edge identifiers, each identifying a second edge that is an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing the second edge ensures that the first edge will be executed. Such a scheme may permit a decision for any pair of control-flow edges or tasks of the workflow graph whether they are sometimes, never, or always reached concurrently. This has different applications in finding control- and data-flow errors.

Abstract: A new technique to analyze the control-flow, i.e., the workflow graph of a business process model, which is called symbolic execution, is provided. Acyclic workflow graphs that may contain inclusive OR-gateways are considered; a symbolic execution for them is defined, which runs in quadratic time. In particular, this symbolic execution essentially comprises labeling edges of nodes of the graph such that a label assigned to a first edge comprises a set of one or more edge identifiers, each identifying a second edge that is an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing the second edge ensures that the first edge will be executed. Such a scheme may permit a decision for any pair of control-flow edges or tasks of the workflow graph whether they are sometimes, never, or always reached concurrently. This has different applications in finding control- and data-flow errors.

Abstract: A new technique to analyze the control-flow, i.e., the workflow graph of a business process model, which is called symbolic execution, is provided. Acyclic workflow graphs that may contain inclusive OR-gateways are considered; a symbolic execution for them is defined, which runs in quadratic time. In particular, this symbolic execution essentially comprises labeling edges of nodes of the graph such that a label assigned to a first edge comprises a set of one or more edge identifiers, each identifying a second edge that is an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing the second edge ensures that the first edge will be executed. Such a scheme may permit a decision for any pair of control-flow edges or tasks of the workflow graph whether they are sometimes, never, or always reached concurrently. This has different applications in finding control- and data-flow errors.

Abstract: A new technique to analyze the control-flow, i.e., the workflow graph of a business process model, which is called symbolic execution, is provided. Acyclic workflow graphs that may contain inclusive OR-gateways are considered; a symbolic execution for them is defined, which runs in quadratic time. In particular, this symbolic execution essentially comprises labeling edges of nodes of the graph such that a label assigned to a first edge comprises a set of one or more edge identifiers, each identifying a second edge that is an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing the second edge ensures that the first edge will be executed. Such a scheme may permit a decision for any pair of control-flow edges or tasks of the workflow graph whether they are sometimes, never, or always reached concurrently. This has different applications in finding control- and data-flow errors.

Abstract: A system and associated method for hierarchically decomposing a workflow graph G into a process structure tree PST. The workflow graph G is a two-terminal graph parsed into a tree T having triconnected components. Boundary pairs of all triconnected components in T are computed and fragments are discovered with boundary pairs. T is restructured into PST pursuant to categories of triconnected components in T. PST is deterministic and modular. PST represents a block-based process model that has fine blocks of execution units. PST is computed in time linear to the number of edges in G.

Abstract: Methods and apparatus are provided for production of a difference log in a data processing system. The difference log defines differences between process models defined in system memory. For each of the process models, model structure data provided in memory defines a hierarchy of SESE regions representing the structure of that model. Also provided in memory are model comparison data defining correspondences between elements of the models, and region comparison data defining correspondences between regions of the SESE region hierarchies for the models. The model comparison and region comparison data are analyzed to identify differences between the SESE region hierarchies, and a difference log defining said differences is produced. In preferred systems, the model structure data and the region comparison data are computed for the models, and the difference log has a hierarchical structure corresponding to the structure of the process models.

Abstract: A new technique to analyze the control-flow, i.e., the workflow graph of a business process model, which is called symbolic execution, is provided. Acyclic workflow graphs that may contain inclusive OR-gateways are considered; a symbolic execution for them is defined, which runs in quadratic time. In particular, this symbolic execution essentially comprises labeling edges of nodes of the graph such that a label assigned to a first edge comprises a set of one or more edge identifiers, each identifying a second edge that is an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing the second edge ensures that the first edge will be executed. Such a scheme may permit a decision for any pair of control-flow edges or tasks of the workflow graph whether they are sometimes, never, or always reached concurrently. This has different applications in finding control- and data-flow errors.

Abstract: A system and associated method for hierarchically decomposing a workflow graph G into a process structure tree PST. The workflow graph G is a two-terminal graph parsed into a tree T having triconnected components. Boundary pairs of all triconnected components in T are computed and fragments are discovered with boundary pairs. T is restructured into PST pursuant to categories of triconnected components in T. PST is deterministic and modular. PST represents a block-based process model that has fine blocks of execution units. PST is computed in time linear to the number of edges in G.

Abstract: Methods and apparatus are provided for production of a difference log in a data processing system The difference log defines differences between process models defined in system memory. For each of the process models, model structure data provided in memory defines a hierarchy of SESE regions representing the structure of that model. Also provided in memory are model comparison data defining correspondences between elements of the models, and region comparison data defining correspondences between regions of the SESE region hierarchies for the models. The model comparison and region comparison data are analyzed to identify differences between the SESE region hierarchies, and a difference log defining said differences is produced. In preferred systems, the model structure data and the region comparison data is computed for the models, and the difference log has a hierarchical structure corresponding to the structure of the process models.

Abstract: A system for combining quality assurance and model transformations in a business-driven development environment includes a host system executing a business modeling application, a transformation framework including a transformation programming interface (TPI) and a quality assurance framework executing on top of the business modeling application, and a plurality of transformation plug-in tools in communication with the TPI. The TPI includes options for model access and traversal, model element creation/removal, model element property editing and analysis. The options are applied to the transformations, via the selected transformation plug-in tools, to a business model resulting in a modified business model that conforms to an information technology (IT)-based executable code.