Abstract: Methods are provided of automatically evolving a process model including process-elements and relationships between process-elements. The method comprises receiving user input defining one or more new constraints to be satisfied by the evolved process model. The method further comprises performing an optimization method to find a best solution of a cost function depending on a set of optimization conditions, over a solution space of possible consistent and/or sound process models derivable from the process model to be evolved depending on the one or more new constraints. The set of optimization conditions comprises maximization of constraints satisfaction corresponding to satisfaction of the one or more new constraints and of previous constrains from previous executions of the method. The method still further comprises updating the process model with the obtained best solution. Computer programs and systems are also provided that are suitable for performing such methods.

Abstract: First data sets associated with a job of a workflow are accessed, each first data set specifying a runtime of a job and a first plurality of feature values of a plurality of features. Feature weighting analyses are executed utilizing the first data sets to rank the plurality of features with respect to their predictive value on whether or not execution of the job is to fail. During execution of the workflow, a runtime of the job of the workflow is monitored. During execution of the job, a likelihood of failure of the job is generated based at least in part on the monitored runtime of the job and a plurality of runtimes of second data sets, the second data sets selected from the first data sets based on the rank of the plurality of features and one or more expected feature values associated with the execution of the job.

Abstract: A browser resource controller combines code metric values with a complexity analysis of rendered content to determine whether resource metric values are appropriate for a web application. The browser resource controller analyzes rendered content of a web application to generate the complexity metric values that represent the complexity of the web application. The browser resource controller also compares executable elements from the web application with exploitative code components from code repositories to determine an exploitative code risk. The browser resource controller determines a resource consumption limit for a web application based on both the exploitative code risk and the complexity metric values and compares the resource consumption limit to a detected resource consumption value.

Abstract: A digital currency driven channel assignment technique is disclosed. Each AP in a distributed network uses a channel selection manager and a distributed ledger to select channels according to a channel assignment and a digital currency associated with the distributed ledger. The digital currency incentivizes APs to make sacrifices in their channel selection for the benefit of the overall network while punishing APs that select channels selfishly and cause bandwidth interference.

Abstract: Provided is a computer system that includes a processor and a memory coupled to the processor, the memory including computer readable program code embodied therein that, when executed by the processor, causes the processor to generate a catalog that identifies a plurality of tasks that a plurality of network resources are available to perform, the network resources including Internet-of-things devices and human network resources and to generate, in response to receiving a request to perform a complex project, a solution path that includes an ordered list corresponding to selected ones of the plurality of tasks that are capable of aggregately performing the complex project, wherein the selected ones of the plurality of tasks define the solution path in an edge graph that include the plurality of tasks represented as edges therein.

Abstract: Determining a similarity between a pair of graphs or patterns can be a computationally expensive and time-consuming process. To reduce the similarity calculation costs, patterns can be simplified based on equivalent classes of components. A similarity score can be calculated between nodes of a pattern. The nodes which represent a same component type and have similar attributes will likely have a high similarity score and can be combined into a single node representing the entire class of the components. The decision to combine nodes also considers a node's topological features such as relationships and connections to other nodes. By combining equivalent nodes, the search space for mapping and determining similarity between two graphs can be reduced. Reducing the search space, exponentially reduces the number of iterations required for determining an optimal similarity score and improves the performance and scalability of the overall root cause analysis framework.

Abstract: To aid in the root cause analysis of current system errors or anomalies, a graph-based root cause analysis software determines whether a graph representing an anomalous region of a system, referred to as a pattern, is similar to a previously stored pattern in a pattern library. The analysis software extracts a sub-graph or pattern representing components currently experiencing an anomaly from an overall system graph. The analysis software calculates a similarity score based on the comparison of the extracted pattern to patterns in the pattern library. The patterns in the pattern library represent previously encountered anomalies and include attributes, event data, expert/system administrator notes, etc., that can aid in diagnosing the current system anomaly.

Abstract: Provided is a computer system that includes a processor and a memory coupled to the processor, the memory including computer readable program code embodied therein that, when executed by the processor, causes the processor to generate a catalog that identifies a plurality of tasks that a plurality of network resources are available to perform, the network resources including Internet-of-things devices and human network resources and to generate, in response to receiving a request to perform a complex project, a solution path that includes an ordered list corresponding to selected ones of the plurality of tasks that are capable of aggregately performing the complex project, wherein the selected ones of the plurality of tasks define the solution path in an edge graph that include the plurality of tasks represented as edges therein.

Abstract: As a network increases in size and complexity, it becomes increasingly difficult to monitor and record relationships between components in the network. The lack of knowledge regarding component relationships can make it difficult to adequately and timely perform analysis of network issues or conditions. As a result, automated generation of a context graph that displays relationships among both hardware and software components in a network can help keep pace with a growing network and improve network analysis. The context graph may be generated based, for example, on event data (alternately referred to as event indications) generated by network components and/or event monitoring agents and network topology information. Additionally, the context graph may be augmented to display inter-component relationships based on multi-event correlations. The context graph can be used to assist in troubleshooting network issues or performing root cause analysis.

Abstract: A first event notification is received. The first event notification is associated with a first event. Criteria is determined based, at least in part, on the first event notification. A first component is identified based, at least in part, on the criteria a component graph. An operational status associated with the first component is determined. It is determined not to generate a second event notification based, at least in part, on the operational status.

Abstract: A process model can be modified to be more precise by unrolling loops of the process model and evaluating or using the process model with the loops unrolled. After determining loops in a process model, sequential forward path executions of each loop identified in an input process model are counted within each trace of an event log. For each loop, a greatest common divisor (gcd) of the sequential forward path execution counts is determined. An intermediate process model is then created with the loops unrolled according to the respective gcd(s). The event log is then (re)played with the intermediate process model to identify traversed elements of the process model. Elements of the intermediate process model that were not traversed are removed to yield a more precise process model.

Abstract: A root cause analysis system utilizes ACs corresponding to component types in a network to construct a diagnosis model. The system generates the ACs based on component models for each component type in the network and may perform offline evaluation of the ACs using determined conditional probabilities and potential state values and cache the results. When an issue is identified at a component, the system uses a relational schema to determine a set of components on which the component depends and creates a diagnosis model for performing root cause analysis. The diagnosis model includes the component type ACs corresponding to each of the components identified in the relational schema. The system populates the diagnosis model with conditional probabilities and observed state values determined from event indications generated by the components. The system outputs a most probable explanation of the issue based on evaluation of the diagnosis model.

Abstract: A first event notification is received. The first event notification is associated with a first event. Criteria is determined based, at least in part, on the first event notification. A first component is identified based, at least in part, on the criteria a component graph. An operational status associated with the first component is determined. It is determined not to generate a second event notification based, at least in part, on the operational status.

Abstract: As a network increases in size and complexity, it becomes increasingly difficult to monitor and record relationships between components in the network. The lack of knowledge regarding component relationships can make it difficult to adequately and timely perform analysis of network issues or conditions. As a result, automated generation of a context graph that displays relationships among both hardware and software components in a network can help keep pace with a growing network and improve network analysis. The context graph may be generated based, for example, on event data (alternately referred to as event indications) generated by network components and/or event monitoring agents and network topology information. Additionally, the context graph may be augmented to display inter-component relationships based on multi-event correlations. The context graph can be used to assist in troubleshooting network issues or performing root cause analysis.

Abstract: As a network increases in size and complexity, it becomes increasingly difficult to monitor and record relationships between components in the network. The lack of knowledge regarding component relationships can make it difficult to adequately and timely perform analysis of network issues or conditions. As a result, automated generation of a context graph that displays relationships among both hardware and software components in a network can help keep pace with a growing network and improve network analysis. The context graph may be generated based, for example, on event data (alternately referred to as event indications) generated by network components and/or event monitoring agents and network topology information. Additionally, the context graph may be augmented to display inter-component relationships based on multi-event correlations. The context graph can be used to assist in troubleshooting network issues or performing root cause analysis.

Abstract: Current behavior can be evaluated to efficiently identify behavioral anomalies with process models of different scopes and/or different degrees of precision. For meaningful behavioral evaluation of an actor (i.e., a user or a device), these multiple process models are constructed with different sets of event logs of a system. A model of a scope of an individual actor and a model of a scope of a group of actors are constructed and used for evaluation. These models of different scope expand “normal” behavior of an actor to include behavior of the group of actors. Although these process models of different scopes likely have different precision, additional models of different precision and/or different scopes can be constructed and used for behavioral evaluation. These different process models allow for behavioral variation within relevant groups of actors.