Abstract: A computer-implemented method, system and computer program product for accurately identifying an execution time of a performance test. Network latency data is grouped into clustered groups of network latency data. Furthermore, the performance test execution times for the same group of performance tests run in the local and remote cluster environments are obtained. The test execution times impacted by network latency (compensation times) are then determined based on such obtained performance test execution times in the local and remote cluster environments. Such compensation times are then grouped into clustered groups of compensation times. A regression model is built to predict a performance test execution time impacted by network latency (compensation time) using the clustered groups of network latency data and compensation times.

Abstract: A computer-implemented method, system and computer program product for generating visualizations for semi-structured data. Visualization data is extracted from infographics depicting semi-structured data. The visualization data that is extracted includes the traits or characteristics of the semi-structured data depicted in the infographics (e.g., dimension), the characteristics of the infographics (e.g., location of the depicted data), and the constraints or display requirements (e.g., display target value in a particular axis). A trait and constraint rule set is then generated based on the extracted visualization data. The trait and constraint rule set includes a set of rules that maps the display requirements to the particular set of traits or characteristics exhibited by the semi-structured data displayed in the infographics.

Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.

Abstract: In an approach for detecting web browsing subject-oriented event interactions and intelligently organizing web pages based on insights from important interactions for better exploration and efficient management, a processor extracts time series data associated with a plurality of web browsing events based on browsing historical actions of a user. A processor identifies the subject of each web browsing event. A processor determines major events based on the time series data and subjects of the plurality of web browsing events. A processor organizes the plurality of web browsing events based on subject hierarchy and timeline from the time series data. A processor highlights one or more uniform resource locators based on the subject hierarchy and timeline.

Abstract: A method includes: receiving, by a computing device, an issue definition of an issue with software; generating, by the computing device and based on the issue definition, an urgency score for the issue, the urgency score representing an urgency of resolving the issue; generating, by the computing device and based on the issue definition, a complexity score for the issue, the complexity score representing a complexity of the issue; identifying, by the computing device using natural language processing and based on the urgency score and the complexity score, an assignee to address the issue, the assignee being a team member of a plurality of team members; recommending, by the computing device, to a user the assignee for assignment to address the issue; and tracking, by the computing device, progress of resolving the issue.

Abstract: An embodiment includes parsing conversation data to extract a message dataset and a user dataset. The embodiment classifies the message dataset into a category using machine learning processing and identifies the category as a top category based at least in part on an amount of the conversation data associated with the category. The embodiment generates impact data associated with the user dataset based on actions in the conversation data by the user. The embodiment generates role data associated with the user by applying a rule to the conversation data for the user. The embodiment generates key index data associated with the message dataset by identifying interactions with a message represented by the message dataset. The embodiment generates output data arranged according to a specified data format that is compatible with a user interface.

Abstract: An approach is provided in which the approach constructs a 3-dimensional (3D) matrix based on a plurality of historical transactions performed by a user. The 3D matrix includes a set of features, a set of rows, and a set of channels. The approach trains a convolutional neural network using the 3D matrix, and then uses the trained convolutional neural network to predict a risk level of a new transaction initiated by the user. The approach transmits an alert message based on the predicted risk level.

Abstract: A computer-implemented method, system and computer program product for accurately identifying an execution time of a performance test. Network latency data is grouped into clustered groups of network latency data. Furthermore, the performance test execution times for the same group of performance tests run in the local and remote cluster environments are obtained. The test execution times impacted by network latency (compensation times) are then determined based on such obtained performance test execution times in the local and remote cluster environments. Such compensation times are then grouped into clustered groups of compensation times. A regression model is built to predict a performance test execution time impacted by network latency (compensation time) using the clustered groups of network latency data and compensation times.

Abstract: An approach is provided in which the approach constructs a 3-dimensional (3D) matrix based on a plurality of historical transactions performed by a user. The 3D matrix includes a set of features, a set of rows, and a set of channels. The approach trains a convolutional neural network using the 3D matrix, and then uses the trained convolutional neural network to predict a risk level of a new transaction initiated by the user. The approach transmits an alert message based on the predicted risk level.

Abstract: A computer-implemented method, system and computer program product for detecting feature traits from an imbalanced dataset. A first regression model is built based on a simulated dataset to compute contribution scores for the features to make a target a positive case. A variance in the features' original values for each feature of the first set of features (those features with contribution scores for positive cases that exceed a threshold value) between the positive and negative cases is determined. A second regression model is built to calculate a predictor importance value for a second set of features (features from the first set of features with a variance in their original values for both positive and negative cases that exceeds a threshold value). Feature traits are then extracted from a group of clustered positive cases with features of the second set of features containing a predictor importance value exceeding a threshold value.

Abstract: An approach is provided in which a method, system, and program product perform a distribution test on a plurality of datasets corresponding to a plurality of predictive features. Each of the plurality of datasets comprises a set of training entries corresponding to a selected one of the plurality of predictive features and a set of testing entries corresponding to the selected predictive feature. The method, system, and program product partition the plurality of predictive features into a differential feature set and a consistent feature set based on their corresponding distribution test. The method, system, and program product generate a final feature set based on the differential feature set and the consistent feature set.

Abstract: In an approach for detecting and profiling influential predictors in a machine learning model, a processor receives a case as an input to a pre-trained model for a prediction, the prediction being based on a plurality of predictors. A processor determines a range for a predictor where the predictor varies without changing the prediction. A processor generates a data set by sampling data across the determined range of the predictor. A processor builds a predictive model with the generated data set. A processor outputs a prediction result including an insight about the range of the predictor.

Abstract: Embodiments relate to a system, computer program product, and method for determining missing values in respective data records with an explanatory analysis to provide a context of the determined values. Such method includes receiving a dataset including incomplete data records that are missing predictors and complete data records. A model is trained with the complete data records and candidate predictors for the missing predictors are generated. A predictor importance value is generated for each candidate predictor and the candidate predictors that have a predictor importance value in excess of a first threshold value are promoted. Respective promoted candidate predictors are inserted into the respective incomplete data records, thereby creating tentative data records. The tentative data records are injected into the model, a fit value is determined for each of the tentative data records, and a tentative data record with a fit value exceeding a second threshold value is selected.

Abstract: A processor may identify a first plurality of transformation nodes from a machine learning pipeline. The processor may couple the first plurality of transformation nodes in series to obtain a sequence of transformation nodes. The processor may select a first transformation node and a second transformation node from the sequence of transformation nodes based on at least one of an input data size and output data size of each of the first plurality of transformation nodes, the second transformation node being subsequent and adjacent to the first transformation node in the sequence of transformation nodes. The processor may obtain an optimized machine learning pipeline by coupling a second plurality of transformation nodes from the machine learning pipeline between the first transformation node and the second transformation node in the sequence of transformation nodes.

Abstract: An approach is provided in which the approach builds a combination model that includes a normal status model and an abnormal status model. The normal status model is built from a set of time-sequenced normal status records and the abnormal status model is built from a set of time-sequenced abnormal status records. The approach computes a set of time-sequenced coefficient combination values of the normal status model and the abnormal status model based on applying a set of fitting coefficient characteristics to the normal status model and the abnormal status model. The approach performs goal seek analysis on a system using the combination model and the set of time-sequenced coefficient combination values.

Abstract: An approach is provided in which a method, system, and program product display, on a user interface, at least one of a set of node split parameters in response to receiving a first user selection that selects a node in a decision tree. The selected node branches to a set of child nodes in the decision tree based on the set of node split parameters. The method, system, and program product adjust at least one of the set of node split parameters of the selected node in response to receiving a second user selection. The method, system, and program product modify the decision tree based on the adjusted set of node split parameters. The modified decision tree includes a modified set of child nodes that branch from the selected node based on the adjusted set of node split parameters.

Abstract: Data segmentation, analysis, and security is provided. An analysis of a set of generated data evolution paths corresponding to a set of data collected over a defined span of time is performed. A behavior trend is determined based on analysis of the set of generated data evolution paths. A set of action steps is performed automatically based on the determined behavior trend.

Abstract: Mechanisms are provided for intelligently identifying an execution environment to execute a computing job. An execution time of the computing job in each execution environment of a plurality of execution environments is predicted by applying a set of existing machine learning models matching execution context information and key parameters of the computing job and execution environment information of the execution environment. The predicted execution time of the machine learning models is aggregated. The aggregated predicted execution times of the computing job are summarized for the plurality of execution environments. Responsive to a selection of an execution environment from the plurality of execution environments based on the summary of the aggregated predicted execution times of the computing job, the computing job is executed in the selected execution environment. Related data during the execution of the computing job in the selected execution environment is collected.

Abstract: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.

Abstract: A method for identifying influential effects that contribute most to a status change of a target index for goal seeking analysis. The method includes generating a candidate list of significant changed predictors between the normal and abnormal status time periods in collected data, and building a plurality of regression models from the collected data. The method determines a first value (trend value or Pearson correlation value) for each of the significant changed predictors based on whether at least one of the significant changed predictors have a significant change trend using the regression models. The method obtains a second predictor importance value for each of the significant changed predictors from a single model built on all the collected data. The method generates a final predictor value for each of the significant changed predictors by combining the first value with the second predictor importance value for each of the significant changed predictors.