Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include receiving data and generating a contextual execution dependency graph with said data. The operations may include producing agents with said data and calculating an agent sequence for said agents based at least in part on said contextual execution dependency graph. The operations may include executing an automation script using said agent sequence and said contextual execution dependency graph.

Abstract: Learned scheduling of autonomous actions includes generating groups of action executions that execute on a collaboration platform. The groups of action executions are generated by natural language processing of contextual information extracted from one or more Chat Operations conversations. Recommendation candidates corresponding to the action executions are generated by clustering action executions contained in each of the groups. The action executions are clustered based on times of past executions. A leaned schedule is generated by ranking the recommendation candidates based on the contextual information. As generated, the learned schedule indicates one or more recommendations to execute a specific action within a specific time.

Abstract: Provided is a computer-implemented method, system, and computer program product for process mining asynchronous support conversations using attributed directly follows graphing. A processor may collect a plurality of conversation threads from an asynchronous data stream. The processor may label each utterance of a plurality of utterances from the plurality of conversation threads with an event label. The processor may analyze the event label for each utterance of the plurality of utterances. The processor may generate, based on the analyzing of the event label for each utterance, an attributed directly follows graph (DFG).

Abstract: An embodiment includes computing a computational cost of a job, using a first machine learning algorithm. The job may include an original amount of an egress of data from a cloud computing environment. The embodiment includes determining, using a second machine learning algorithm, the amount of the egress of data corresponding to the job has a computer business criticality that exceeds a threshold level of business criticality. The embodiment includes analyzing a current egress plan used in computing the computational cost of the job. The embodiment includes reconfiguring the current plan to a second egress plan to reduce the computational cost of the job. The embodiment includes implementing a second plan such responsive to execution of the job, the second plan causes data egress behavior to change from the original egress of data behavior. A modified egress behavior causes an effective reduction in the egress cost of the job.

Abstract: A method including employing corporate data to generate an acyclic graph representing a reporting hierarchy, identifying relevant key performance indicators (KPIs) for a plurality of users, training a classifier to predict whether a KPI is relevant to a user of the plurality of users, generating interest-persona maps correlating KPI impact alerts with a subset of the plurality of users based on detected information technology (IT) event streams, business streams, KPI streams, and environmental metrics streams, assigning a score to each KPI impact alert based on compatibility between user features and KPI features, and notifying, by consulting the interest-persona maps, one or more targeted users of the KPI impact alerts relevant to them.

Abstract: Mechanisms are provided for forecasting information technology (IT) and environmental impacts on key performance indicators (KPIs). Machine learning (ML) computer model(s) are trained on historical data representing events and KPIs of organizational processes (OPs). The ML computer model(s) forecast KPI impact given events. Correlation graph data structure(s) are generated that map at least one of events to IT computing resources, or KPI impacts to OPs. A unified model is trained to model OPs and IT resources. The trained ML computer model(s) and unified model process input data to generate a forecast output that specifies at least one of a forecasted IT event or a KPI impact. The forecasted output is correlated with at least one of IT computing resource(s) or OP(s), at least by applying the correlation graph data structure(s) to the forecast output to generate a correlation output.

Abstract: Mechanisms are provided for forecasting information technology (IT) and environmental impacts on key performance indicators (KPIs). Machine learning (ML) computer model(s) are trained on historical data representing IT events and KPIs of organizational processes (OPs). The ML computer model(s) forecast IT events given KPIs, or KPI impact given IT events. Correlation graph data structure(s) are generated that map at least one of IT events to IT computing resources, or KPI impacts to OPs. The trained ML computer model(s) process input data to generate a forecast output that specifies at least one of a forecasted IT event or a KPI impact. The forecasted output is correlated with at least one of IT computing resource(s) or OP(s), at least by applying the correlation graph data structure(s) to the forecast output to generate a correlation output. A remedial action recommendation is generated based on the forecast output and correlation output.

Abstract: Aspects of the present disclosure relate to audio/video (A/V) stream functionality verification. A stream segment of a video feed prior to transmission over a network as captured by a transmitting device within a web-based conference can be stored. A stream segment of the video feed after transmission over the network as received by a receiving device within the web-based conference can be stored. The stream segment of the video feed prior to transmission over the network can be compared with the stream segment of the video feed after transmission over the network to determine a video feed quality.

Abstract: Mechanisms are provided for automated generation of an electronic form for an electronic messaging subsystem. Historical conversation logs are obtained from the electronic messaging subsystem which comprise a plurality of communication sequences. Communication sequences within the historical conversation logs are clustered according to similarity of features. For each cluster the following operations are performed: identifying, within the cluster, sequences of normalized utterances that are repeated across communication sequences of the cluster; categorizing each sequence, in a set of the sequences of normalized utterances, as to whether the sequence can be represented as one or more electronic forms; and extracting, for each communication sequence in the cluster, attributes and corresponding attribute values. One or more electronic form data structures are generated based on the attributes and corresponding attribute values extracted for each communication sequence in the cluster.

Abstract: Predicting the impact of an information technology (IT) failure includes detecting a computer-generated indication of the failure. Responsive to determining that the IT failure is a previously unseen IT failure, operations of a computer-implemented unseen event handler can be invoked. The unseen event handler can map the previously unseen IT failure to a previously seen IT failure based on a similarity score generated by a computer-implemented similarity scorer, wherein the similarity score is based on a unified process-IT topology. A machine learning model can generate an IT failure impact prediction and recommendation based on the mapping, wherein the machine learning model also is based on the unified process-IT topology. An output of the IT failure prediction and recommendation can be generated.

Abstract: Scheduling IT maintenance based on key performance indicators (KPIs) includes receiving current information technology (IT) data corresponding to an IT system and current process data corresponding to a process implemented with the IT system. A suffix and time prediction model generates a suffix prediction of a likely sequence of suffixes given a prefix of the process and a time prediction of time remaining to complete the likely sequence of suffixes, wherein the suffix and time predictions are based on based on the IT data and current process data; A load on each of step of the process is determined based on the suffix and time prediction; One or more KPI metrics of the process is determined based on the load of each step. An IT maintenance schedule output is generated based on the one or more KPI metrics.

Abstract: Mechanisms are provided for generating, executing, orchestrating, and monitoring an information technology (IT) incident remediation task workflow. An IT incident notification is received and a knowledge data structure associated with an IT resource corresponding to the IT incident is retrieved. IT remediation task(s) are extracted from the knowledge data structure and correlated with skills in a plurality of predetermined skills. Automated tools are correlated with corresponding skills in the plurality of predetermined skills. An IT incident remediation task workflow is generated based on a matching of skills associated with the IT remediation tasks and automation tools. The generated IT incident remediation task workflow is automatically executed on the at least one IT resource.

Abstract: Mechanisms are provided for forecasting information technology (IT) and environmental impacts on key performance indicators (KPIs). Machine learning (ML) computer model(s) are trained on historical data representing IT events and KPIs of organizational processes (OPs). The ML computer model(s) forecast IT events given KPIs, or KPI impact given IT events. Correlation graph data structure(s) are generated that map at least one of IT events to IT computing resources, or KPI impacts to OPs. The trained ML computer model(s) process input data to generate a forecast output that specifies at least one of a forecasted IT event or a KPI impact. The forecasted output is correlated with at least one of IT computing resource(s) or OP(s), at least by applying the correlation graph data structure(s) to the forecast output to generate a correlation output. A remedial action recommendation is generated based on the forecast output and correlation output.

Abstract: Methods, systems, and computer program products for dynamic pricing of energy consumed from a shared battery using real-time consumption data are provided herein. A computer-implemented method includes calculating wear cost arising from a battery shared by multiple users, wherein the wear cost is based on usage data of the battery; calculating a proportionality factor for each of the multiple users for the calculated wear cost, wherein the proportionality factor is based on individual usage of the battery; apportioning the calculated wear cost to each of the multiple users based on the user's proportionality factor; and determining a dynamic price for energy used by the battery for each of the multiple users, based on said apportioning.

Abstract: Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention enrich received information based on identified attributes. Embodiments of the present invention can then dynamically generate a recommendation that satisfies a goal based, at least in part on the enriched information. Embodiments of the present invention can then execute at least one dynamically generated goal that satisfies the goal.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include receiving data and generating a contextual execution dependency graph with said data. The operations may include producing agents with said data and calculating an agent sequence for said agents based at least in part on said contextual execution dependency graph. The operations may include executing an automation script using said agent sequence and said contextual execution dependency graph.

Abstract: Provided is a computer-implemented method, system, and computer program product for process mining asynchronous support conversations using attributed directly follows graphing. A processor may collect a plurality of conversation threads from an asynchronous data stream. The processor may label each utterance of a plurality of utterances from the plurality of conversation threads with an event label. The processor may analyze the event label for each utterance of the plurality of utterances. The processor may generate, based on the analyzing of the event label for each utterance, an attributed directly follows graph (DFG).

Abstract: Aspects of the present disclosure relate to audio/video (A/V) stream functionality verification. A stream segment of a video feed prior to transmission over a network as captured by a transmitting device within a web-based conference can be stored. A stream segment of the video feed after transmission over the network as received by a receiving device within the web-based conference can be stored. The stream segment of the video feed prior to transmission over the network can be compared with the stream segment of the video feed after transmission over the network to determine a video feed quality.

Abstract: Methods, systems, and computer program products for generating automation recommendations for ad hoc processes are provided herein. A computer-implemented method includes obtaining workflow data comprising descriptions associated with one or more dynamic processes; creating event logs based at least in part on the descriptions; applying a graph extraction process to derive process flow graphs from the created event logs; generating embeddings of the process flow graphs, wherein the embeddings encode at least one of: one or more structural features and one or more attribute features of the process flow graphs; and identifying at least one of the process flow graphs to be automated based on the generated embeddings; and outputting the identified at least one process flow graph to at least one of: a user and a robotic process automation tool.

Abstract: Methods, systems, and computer program products for generating organizational goal-oriented and process conformant recommendation models using artificial intelligence techniques are provided herein.