Abstract: A method, a computer program product and a computer system refine Internet search recommendations. The method includes receiving a search input from a user. The method also includes receiving a plurality of sets of search results from respective search engines. Each search engine utilizes a respective type of search process. Each of the sets of the search results for a selected one of the search engines is prioritized according to the type of the search process. The method further includes applying respective weights to the search engines such that the sets of search results have a modified priority based on the weights. The weights are associated with the user. Finally, the method includes generating modified search results based on the sets of search results, the weights, and the modified priority.

Abstract: Performing a goal-seek analysis of spatial-temporal data by generating a hierarchical cluster according to spatial temporal data, determining a spatial-temporal location input for a target, determining spatial-temporal predictor values for the spatial-temporal location, and adjusting the hierarchical cluster according to and the spatial-temporal predictors.

Abstract: Test case selection methods are disclosed. A feature of a candidate test case and respective features of a set of test cases are extracted. The set of test cases is clustered into a plurality of clusters based on the respective features of the set of test cases. At least one cluster related to the candidate test case is determined from the plurality of clusters based on the feature of the candidate test case. At least one test case similar to the candidate test case is selected from a plurality of test cases included in the at least one cluster.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for estimating the results of a performance test on an updated software application. A method, the method comprising receiving an updated software application, wherein the size of the updated software application is a first size and generating a plurality of small probe, wherein the size of each of the small probe data is a second size, wherein the second size is less than the first size. Conducting a first performance test on the plurality of small probe data and calculating an estimated elapsed time for a performance test on the updated software application. Conducting the performance test on the updated software application and determining if the updated software is given a PASS or FAIL for the performance test, based in part on the elapsed time of the performance test on the updated software application.

Abstract: Test case selection methods are disclosed. A feature of a candidate test case and respective features of a set of test cases are extracted. The set of test cases is clustered into a plurality of clusters based on the respective features of the set of test cases. At least one cluster related to the candidate test case is determined from the plurality of clusters based on the feature of the candidate test case. At least one test case similar to the candidate test case is selected from a plurality of test cases included in the at least one cluster.

Abstract: One or more computer processors group a plurality of predictors contained in training data into a plurality of predictor groups. The one or more computer processors create a plurality of sample sets, wherein each sample set in the plurality of sample sets contains one or more predictors selected from a respective predictor group in the plurality of predictor groups. The one or more computer processors create a cluster model for each created sample set in the plurality of created sample sets. The one or more computer processors generate a score for a record with one or more missing values utilizing at least one created cluster model of the created cluster models and at least one created sample set of the created sample sets.

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 method, system, and computer program product are provided for key performance indicator (KPI) extraction. A baseline value and times series data are received. The time series data includes logs, performance data, and operational data from one or more servers. The time series data is embedded to a vector. A multi-tier list of key KPI values is created. The key KPI value having a least cumulative absolute error is identified.

Abstract: Embodiments generally relate to determining traffic congestion patterns. In some embodiments, a method includes identifying congestion events for each road of a plurality of roads in a road network, where each congestion event indicates a drop in average vehicle speed below a predetermined speed threshold for a particular road in the road network, and where the congestion events span a predetermined time period. The method further includes determining local clusters of the congestion events based on one or more road condition parameters, where each local cluster defines a local congestion pattern for a particular road of the plurality of roads in the road network. The method further includes grouping the local clusters into one or more global clusters based on the one or more road condition parameters, where the global clusters define global congestion patterns in the road network.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.

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: Embodiments for managing feature engineering with relational data are provided. A graphical user interface (GUI) that provides a user with the ability to upload a plurality of tables, select joins between the plurality of tables, and select keys for the joins is provided. Responsive to receiving user input indicative of selecting joins between the plurality of tables and selecting keys for the joins utilizing the GUI, the user selections are automatically validated and actions associated with at least some of the plurality of tables are dynamically performed based on the user selections. Information associated with the user selections and the validating is provided. The information includes a recommendation to link a third key in the at least some of the plurality of tables to a fourth key in the at least some of the plurality of tables.

Abstract: A method, computer system, and a computer program product for detecting a plurality of influential factors for traffic congestion is provided. The present invention may include identifying one or more influential roads and one or more influential routings associated with an object road in a target road network. The present invention may also include quantifying an impact associated with the identified one or more influential roads and the identified one or more influential routings associated with the object road, wherein at least one Impact Matrix is utilized to determine a road-by-road influence in connection to the target road network.

Abstract: Embodiments for managing feature engineering with relational data are provided. A graphical user interface (GUI) that provides a user with the ability to upload a plurality of tables, select joins between the plurality of tables, and select keys for the joins is provided. Responsive to receiving user input indicative of selecting joins between the plurality of tables and selecting keys for the joins utilizing the GUI, the user selections are automatically validated and actions associated with at least some of the plurality of tables are dynamically performed based on the user selections. Information associated with the user selections and the validating is provided. The information includes a recommendation to link a third key in the at least some of the plurality of tables to a fourth key in the at least some of the plurality of tables.

Abstract: According to an embodiment, a method, computer system, and computer program product for managing data is provided. The present invention may include accumulating a plurality of predicted outputs according to a data accumulation rule. The plurality of predicted outputs is generated by a predictive model executed by a first system. The present invention may include evaluating, by a second system, an accuracy of the predictive model. Evaluating the accuracy of the predictive model may include determining a degree of difference between the plurality of predicted outputs and information generated during a development stage of the predictive model. The present invention may include determining whether the accuracy of the predictive model has declined by an amount which exceeds a pre-determined threshold. The present invention may include updating the predictive model.

Abstract: A computer-implemented method, system and computer program product for generating personalized recommendations to address a target problem. A machine learning prediction model directed to a target problem for an individual is built with historical data. After receiving data about the individual, a prediction for the individual is obtained in connection with the target problem by the built model using the received data about the individual. Key predictors (e.g., parameters) and their weight for the individual are generated using the prediction by an explanation model. Record(s) are identified from the historical data by performing similarity analysis of the historical data using the key predictors and their weight. Such records provide a population closely related to the individual with respect to the target problem. These records are then analyzed and recommendations are provided to a user to solve the target problem for the individual based on the analysis of the identified record(s).

Abstract: Automated development and training of deep forest models for analyzing data by growing a random forest of decision trees using data, determining Out-of-bag (OOB) predictions for the forest, appending the OOB predictions to the data set, and growing an additional forest using the data set including the appended OOB predictions, and combining the output of the additional forest, then utilizing the model to classify data outside the training data set.

Abstract: A method, computer system, and a computer program product for detecting a plurality of influential factors for traffic congestion is provided. The present invention may include identifying one or more influential roads and one or more influential routings associated with an object road in a target road network. The present invention may also include quantifying an impact associated with the identified one or more influential roads and the identified one or more influential routings associated with the object road, wherein at least one Impact Matrix is utilized to determine a road-by-road influence in connection to the target road network.

Abstract: Embodiments generally relate to determining traffic congestion patterns. In some embodiments, a method includes identifying congestion events for each road of a plurality of roads in a road network, where each congestion event indicates a drop in average vehicle speed below a predetermined speed threshold for a particular road in the road network, and where the congestion events span a predetermined time period. The method further includes determining local clusters of the congestion events based on one or more road condition parameters, where each local cluster defines a local congestion pattern for a particular road of the plurality of roads in the road network. The method further includes grouping the local clusters into one or more global clusters based on the one or more road condition parameters, where the global clusters define global congestion patterns in the road network.

Abstract: According to an embodiment, a method, computer system, and computer program product for managing data is provided. The present invention may include accumulating a plurality of predicted outputs according to a data accumulation rule. The plurality of predicted outputs is generated by a predictive model executed by a first system. The present invention may include evaluating, by a second system, an accuracy of the predictive model. Evaluating the accuracy of the predictive model may include determining a degree of difference between the plurality of predicted outputs and information generated during a development stage of the predictive model. The present invention may include determining whether the accuracy of the predictive model has declined by an amount which exceeds a pre-determined threshold. The present invention may include updating the predictive model.