Abstract: Computer-implemented methods for discovery and reuse of a high value pipeline segment are provided. Aspects include defining a set of datasets associated with a processing pipeline based on a set of data operations of the processing pipeline. Aspects also include generating a library of pipeline segments based on the processing pipeline and at least one dataset of the set of datasets. In some aspects, generating the library of pipeline segments includes adding a pipeline segment of the processing pipeline to the library based on one or more characteristics of a dataset generated by the pipeline segment, where the dataset is included in the set of datasets.

Abstract: Controlling application logging, including: identifying one or more relationships between a plurality of classes of an object oriented application; generating a tree structure comprised of a plurality of nodes and a plurality of branches, where each of the plurality of branches is comprised of at least one node from the plurality of nodes, based on the one or more identified relationships, where a first one or more nodes of the plurality of nodes correspond to one or more classes of the object-oriented application; assigning a weight to each of the plurality of branches, based at least in part on an identified relationship corresponding to each of the one or more nodes comprising each of the plurality of branches; and determining a logging level to each branch of the plurality of branches, based on the assigned weight for each branch of the plurality of branches.

Abstract: A method for dynamically adjusting a proxy server timeout includes identifying each microservice within a proxy server and mapping a topology of microservices, assigning each microservice to one of an application layer, a middleware layer and an infrastructure layer. Defining each representational state transfer (REST) application programming interface (API) calling relationship between each microservice and each other microservice. Determining a corresponding regression model defining a response time of each microservice based at least in part on a set of available response time predictors. Building a sequence model for at least one microservice in the application layer. Predicting an incoming REST API call and identifying a probable sequence model corresponding to the predicted incoming REST API call. Updating a timeout value of the predicted REST API call within the proxy server based on the sequentially predicted response times.

Abstract: Computer technology for protecting data security in a computerized system for recommending content to users where, a processing unit generates an identifier for a first data record relating to a user device based on a first machine learning model. Then, the processing unit sends the identifier to a service provider, and the service provider uses the identifier to determine one or more contents to be sent to the user device. Creating and using a decision tree machine learning (ML) model and a cluster ML model with training records and a transformed records.

Abstract: A computer-implemented method, program product, and systems for managing distributed processes is provided. Embodiments of the present invention define additional system parameters to a kernel using a union structure. Embodiments of the present invention can then process information manager kernel using a cluster model using a formed distance between one node of the plurality and another node of the plurality. Embodiments of the present invention can then control an interval for information collection and sorting frequency for each node of the plurality of nodes in the system.

Abstract: Determining missing relationship information and augmenting a knowledge graph includes obtaining a knowledge graph with nodes representing entities and edges representing relationships between related entities, building a dataset indicating attribute(s) and attribute value(s) of each entity of a related entity pair, a relationship type of the relationship between the related entity pair, and a weight of the relationship type, using the dataset to build machine learning model(s), receiving a partial specification of a new relationship triple, the specification including the new source entity and missing relationship information of the new relationship triple, applying one or more of the model(s) and identifying the missing relationship information, and augmenting the graph to provide an augmented knowledge graph that includes the new relationship triple, including the new source entity, the target entity, and the relationship type of the relationship between the new source entity and the target entity.

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: Techniques are described with regard to user interface configuration in a computing environment. An associated computer-implemented method includes initializing an element layout within a set of user interface layers for a certain user based upon random determination, wherein the element layout includes a plurality of elements on which the certain user operates. Responsive to determining that a predefined user history data threshold is exceeded, the method further includes deriving weight metrics for the plurality of elements in association with each of the set of user interface layers based upon user history data, applying at least one layout mode to respective elements among the plurality of elements associated with each of the set of user interface layers based upon the derived weight metrics, and updating the element layout within each of the set of user interface layers for the certain user consequent to applying the at least one layout mode.

Abstract: The present disclosure relates to privacy protection in a search process. According to a method, a target emotion vector is extracted from a search interaction, the target emotion vector representing emotional information in the search interaction. Respective emotion distances between the target emotion vector and respective emotion vectors associated with a plurality of text clusters are determined. The plurality of text clusters is clustered from a dictionary of text elements. A first number of text clusters are selected from the plurality of text clusters based on the determined respective emotion distances. The first number of text clusters have emotion distances larger than at least one unselected text cluster among the plurality of text clusters. A plurality of confused search interactions are constructed for the search interaction based on the first number of text clusters, and the plurality of confused search interactions are performed.

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.

Abstract: An embodiment for an improved method of automated version management for machine learning pipeline development is provided. The embodiment may compute a quality value of a target machine learning pipeline at a predetermined regular interval and automatically save updated versions of the target machine learning pipeline. The embodiment may extract a series of key features from detected versions of the target machine learning pipeline and cluster the detected versions of the target machine learning model pipeline by the extracted series of key features. The embodiment may identify a highest-quality version within each of a series of generated clusters. The embodiment may compute similarity scores for subsets of versions within each of the series of generated clusters. The embodiment may generate and output to a user, a visual representation of the series of generated clusters including version quality data and version similarity data.

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: Embodiments of the present disclosure relate to a method, system and computer program product for semantic search based on a graph database. In some embodiments, a method is disclosed. According to the method, the user jobs of a user are obtained from a first software product. Based on the user jobs, target test cases are selected from a plurality of test cases associated with the first software product and a second software product. The target test cases are applied to the first software product and the second software product, and in accordance with a determination that a result of applying the target test cases satisfies a predetermined criterion, an instruction is provided to indicate migrating from the first software product to the second software product. In other embodiments, a system and a computer program product are disclosed.

Abstract: At least one processor identifies dependency relationships among libraries in a repository of libraries. Using the dependency relationships among libraries, at least one machine learning model can be created that predicts with a confidence value a dependency between a given library and a target library. An L layer tree-like graph can be created, using the dependency relationships among libraries and an application package. L can be configurable. Versions of the libraries to use can be determined by running the at least one machine learning model for each pair of nodes having a dependency relationship in the L layer tree-like graph, the at least one machine learning model identifying the dependency relationship with a confidence value, where pairs of nodes having largest confidence values are selected as the versions of the libraries to use in the application package.

Abstract: Embodiments of the present disclosure relate to a method, system and computer program product for semantic search based on a graph database. In some embodiments, a method is disclosed. According to the method, the user jobs of a user are obtained from a first software product. Based on the user jobs, target test cases are selected from a plurality of test cases associated with the first software product and a second software product. The target test cases are applied to the first software product and the second software product, and in accordance with a determination that a result of applying the target test cases satisfies a predetermined criterion, an instruction is provided to indicate migrating from the first software product to the second software product. In other embodiments, a system and a computer program product are disclosed.

Abstract: Computer technology for protecting data security in a computerized system for recommending content to users where, a processing unit generates an identifier for a first data record relating to a user device based on a first machine learning model. Then, the processing unit sends the identifier to a service provider, and the service provider uses the identifier to determine one or more contents to be sent to the user device. Creating and using a decision tree machine learning (ML) model and a cluster ML model with training records and a transformed records.

Abstract: The present disclosure relates to privacy protection in a search process. According to a method, a target emotion vector is extracted from a search interaction, the target emotion vector representing emotional information in the search interaction. Respective emotion distances between the target emotion vector and respective emotion vectors associated with a plurality of text clusters are determined. The plurality of text clusters is clustered from a dictionary of text elements. A first number of text clusters are selected from the plurality of text clusters based on the determined respective emotion distances. The first number of text clusters have emotion distances larger than at least one unselected text cluster among the plurality of text clusters. A plurality of confused search interactions are constructed for the search interaction based on the first number of text clusters, and the plurality of confused search interactions are performed.

Abstract: Examples described herein provide a computer-implemented method that includes determining a kernel width for the machine learning model. The method further includes building a local interpretable linear model using the kernel width. The method further includes computing a contribution and confidence for a feature of the local interpretable linear model. The method further includes updating the local interpretable linear model to generate a final model and computing an overall confidence for the final model.

Abstract: A method, computer program, and computer system are provided for training a machine learning model. A feature associated with training data derived from a dataset is identified. A machine learning model is generated based on the training data. At least a portion of the training data associated with maximizing an importance value associated with the identified feature is selected. The importance value corresponds to a need associated with the machine learning model. One or more weight values is assigned to the selected portion of the training data. The machine learning model is updated based on the assigned weight values.

Abstract: A method, a structure, and a computer system for object trail analytics. The exemplary embodiments may include obtaining time series data detailing an average speed of one or more roads within a traffic network at one or more times. The exemplary embodiments may further include extracting one or more features corresponding to the time series data, and generating one or more time series forecasting models based on the time series data and the one or more features. Additionally, the exemplary embodiments may include identifying a current location of a moving object within the traffic network, and predicting a speed of the moving object based on applying the one or more time series forecasting models to the current location.