Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

Abstract: An approach is provided in which the approach segments each one of multiple components corresponding to multiple component levels in an SQL database system into multiple functions. The approach combines a first one of the multiple functions with a second one of the multiple functions into an image, and invokes the image to process an SQL query using the first function and the second function.

Abstract: The invention provides a federated model based on locally trained machine learning models.

Abstract: Embodiments are disclosed for a method. The method includes determining demand resource for a batch jobs using a resource machine learning model trained to determine a cloud resource that the batch jobs use more than other resources during execution. The method further includes generating resource estimates for the demand resources. Additionally, the method includes determining a batch rating for a batch run using a batch rating machine learning model that is trained to generate a batch rating number based on features representing a priority of the batch run in reference to parallel execution batch runs. The method also includes generating a purchase recommendation for execution of the batch run on a cloud platform based on the resource estimates and the batch rating.

Abstract: A lightweight database-management system (DBMS) is based on a dynamic microservices architecture that implements each granular DBMS feature or function as a distinct, independently executable microservice. The DBMS's Parser front-end responds to each incoming query by selecting the first bind-time database feature needed to process the query. The Parser forwards its selection through a Channel-Binding subsystem to an Event Services Activation subsystem that activates a corresponding microservice to perform the selected feature. The first feature then selects the next required bind-time feature for activation, and this process continues sequentially until all required bind-time microservices have been identified, activated, and run. Runtime query-processing features are then sequentially selected in a similar manner. However, each selected runtime microservice is preloaded but not run.

Abstract: Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

Abstract: A method, system, and computer program product for abnormal data detection. According to the method, a plurality of data points collected at different time points are classified into a plurality of groups. A plurality of groups of potential abnormal data points are determined from the plurality of groups. Correlations between a first group of the plurality of groups of potential abnormal data points with other groups of potential abnormal data points are determined. In response to the first group of the plurality of groups of potential abnormal data points being uncorrelated to a majority of the other groups of potential abnormal data points based on the correlations, data points in the first group are identified as abnormal data points.

Abstract: A tool for combining common processes shared by at least two or more sub-queries within a query is provided. The tool determines whether one or more sub set relationships are shared between the at least two or more sub-queries. Responsive to a determination that one or more sub set relationships are shared between the at least two or more sub-queries, the tool determines an order class for the at least two or more sub-queries based on the one or more sub set relationships, wherein determining the order class includes transforming the query to include one or more differing aspects within the single shared common process, with the one or more differing aspects arranged based, at least in part, on a query style, a query type, and a query function. Responsive to determining an access path for the query, the tool executes the access path during run-time for data accessing.

Abstract: Database configuration tuning is provided. A set of database nodes having similar data factors is selected in a centerless network of database nodes. Configuration models corresponding to the set of database nodes are trained using data parallelism. Trained configuration models corresponding to the set of database nodes are combined to form a federated configuration model. It is determined whether performance indicators corresponding to the set of database nodes are greater than a performance threshold level. In response to determining that the performance indicators corresponding to the set of database nodes are greater than the performance threshold level, a database configuration corresponding to the federated configuration model is recommended to a new database node. The new database node is joined to the centerless network.

Abstract: A lightweight database-management system (DBMS) is based on a dynamic microservices architecture that implements each granular DBMS feature or function as a distinct, independently executable microservice. The DBMS's Parser front-end responds to each incoming query by selecting the first bind-time database feature needed to process the query. The Parser forwards its selection through a Channel-Binding subsystem to an Event Services Activation subsystem that activates a corresponding microservice to perform the selected feature. The first feature then selects the next required bind-time feature for activation, and this process continues sequentially until all required bind-time microservices have been identified, activated, and run. Runtime query-processing features are then sequentially selected in a similar manner. However, each selected runtime microservice is preloaded but not run.

Abstract: Database configuration tuning is provided. A set of database nodes having similar data factors is selected in a centerless network of database nodes. Configuration models corresponding to the set of database nodes are trained using data parallelism. Trained configuration models corresponding to the set of database nodes are combined to form a federated configuration model. It is determined whether performance indicators corresponding to the set of database nodes are greater than a performance threshold level. In response to determining that the performance indicators corresponding to the set of database nodes are greater than the performance threshold level, a database configuration corresponding to the federated configuration model is recommended to a new database node. The new database node is joined to the centerless network.

Abstract: Embodiments of the present invention provide efficient systems and methods for searching a keyword. Embodiments of the present invention can be used to more quickly and comprehensively search a document for a keyword or phrase, based on the preferences and behaviors of a user, so that the returned results to the user are ranked in order of the preferences of the user. By factoring in user data such as time spent on certain content and highlighted content, the system is able to more efficiently return preferred results of a user of a keyword search.

Abstract: Embodiments of the present invention provide efficient systems and methods for searching a keyword. Embodiments of the present invention can be used to more quickly and comprehensively search a document for a keyword or phrase, based on the preferences and behaviors of a user, so that the returned results to the user are ranked in order of the preferences of the user. By factoring in user data such as time spent on certain content and highlighted content, the system is able to more efficiently return preferred results of a user of a keyword search.

Abstract: A computer-implemented method includes detecting a first user is editing a first version of a content piece. A first feature set value is determined, describing a first set of values of one or more features of the first version. Based on the first feature set value, a first maturity score of the first version is determined, using a mapping. It is detected that a second user is editing a second version of the content piece in parallel with the first user editing the first version. A second feature set value is determined, describing a second set of values of the one or more features of the second version of the content piece. Based on the second feature set value, a second maturity score of the second version is determined, using the mapping. It is detected that the second maturity score meets a maturity threshold. Thus, a remedial action is performed.

Abstract: A computer-implemented method includes detecting a first user is editing a first version of a content piece. A first feature set value is determined, describing a first set of values of one or more features of the first version. Based on the first feature set value, a first maturity score of the first version is determined, using a mapping. It is detected that a second user is editing a second version of the content piece in parallel with the first user editing the first version. A second feature set value is determined, describing a second set of values of the one or more features of the second version of the content piece. Based on the second feature set value, a second maturity score of the second version is determined, using the mapping. It is detected that the second maturity score meets a maturity threshold. Thus, a remedial action is performed.

Abstract: A tool for combining common processes shared by at least two or more sub-queries within a query is provided. The tool determines whether one or more sub set relationships are shared between the at least two or more sub-queries. Responsive to a determination that one or more sub set relationships are shared between the at least two or more sub-queries, the tool determines an order class for the at least two or more sub-queries based on the one or more sub set relationships, wherein determining the order class includes transforming the query to include one or more differing aspects within the single shared common process, with the one or more differing aspects arranged based, at least in part, on a query style, a query type, and a query function. Responsive to determining an access path for the query, the tool executes the access path during run-time for data accessing.

Abstract: A tool for combining common processes shared by at least two or more sub-queries within a query is provided. The tool determines the query with the at least two or more sub-queries. The tool determines whether one or more sub set relationships are shared between the at least two or more sub-queries. Responsive to a determination that one or more sub set relationships are shared between the at least two or more sub-queries, the tool determines an order class for the at least two or more sub-queries based on the one or more sub set relationships. The tool determines an access path for the query. The tool executes the access path during run-time for data accessing.

Abstract: A method for providing application assistants on applications is provided. The method may include performing pattern analyses on the applications, wherein application features are collected. The method may include determining application pattern types based on the pattern analyses. The method may include determining whether user customizations associated with the determined application pattern types are received. The method may include in response to the determination that user customizations are not received, associating the determined application pattern types with the applications, and generating application assistants based on the associated determined application pattern types. The method may include in response to the determination that user customizations are received, associating the user customizations with the applications, and generating application assistants based on the associated user customizations.

Abstract: Embodiments of the present invention provide efficient systems and methods for searching a keyword. Embodiments of the present invention can be used to more quickly and comprehensively search a document for a keyword or phrase, based on the preferences and behaviors of a user, so that the returned results to the user are ranked in order of the preferences of the user. By factoring in user data such as time spent on certain content and highlighted content, the system is able to more efficiently return preferred results of a user of a keyword search.

Abstract: Embodiments of the present invention provide efficient systems and methods for searching a keyword. Embodiments of the present invention can be used to more quickly and comprehensively search a document for a keyword or phrase, based on the preferences and behaviors of a user, so that the returned results to the user are ranked in order of the preferences of the user. By factoring in user data such as time spent on certain content and highlighted content, the system is able to more efficiently return preferred results of a user of a keyword search.