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: Methods and systems for process analysis include determining attributes of an event log relating to a process. Attributes of an end task of the process are determined based on a comparison of a vector representation of the end task to vector representations to a set of criteria relating to process representations. Hyper-parameters of process mining models are tuned based on the attributes of the event log and the attributes of the end task. The process mining models are ranked based on the attributes of the event log and the attributes of the end task. The event log is mined using a top-ranked process mining model to generate a process representation. An inefficiency of the process is identified based on the process representation. The inefficiency is automatically corrected.

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

Abstract: Methods and arrangements for performing a tree-based asset search. An asset model is assimilated. The asset model is transformed into an asset tree search model. An asset search query is accepted and the asset search query is parsed. An asset search is conducted via applying the parsed asset search query to the asset tree model.

Abstract: Methods and arrangements for dynamic trendline generation. Input data are assimilated at a capture timepoint from an estimation tool. The input data are filtered, and an estimation-centric map is created from the filtered data ; this creating of a map includes generating an estimation data tree. A trendline is dynamically generated responsive to a request, wherein this generating of a trendline includes applying the estimation data tree to the input data. The dynamically generated trendline is supplied to the estimation tool.

Abstract: Methods and systems for automatically generating a service oriented architecture (SOA) design. A set of business process maps for the domain under consideration is defined and a design quality goal (function) that should be met (optimized) is specified. The design goal/function involves SOA metrics like coupling, cohesion, granularity, etc., which the system under consideration is pre-programmed to compute on any SOA design. The system takes as input the set of business process maps and the design quality goal/functions. It first generates semantic business process maps by identifying key concepts that occur in the task and business item descriptions. Next, it efficiently searches the service design space by starting with a seed design and employing a sequence of moves to iteratively optimize it. It outputs a set of possible SOA designs that meet the specified quality goals or optimizes the specified function, from where a user may select the final design.

Abstract: Methods and arrangements for performing a tree-based asset search. An asset model is assimilated. The asset model is transformed into an asset tree search model. An asset search query is accepted and the asset search query is parsed. An asset search is conducted via applying the parsed asset search query to the asset tree model.