Abstract: Disclosed in the present application are a task scheduling method and apparatus, and a terminal device and a storage medium. When tasks to be executed are generated, said tasks are firstly stored in task scheduling queues; a terminal determines a queue scheduling sequence for the task scheduling queues according to the execution priority levels of the task types of said tasks; and an arrangement sequence of said tasks is determined according to the queue scheduling sequence. In this way, the terminal can preferentially process a task to be executed in the foreground, thereby effectively controlling a processing time delay of the task to be executed in the foreground.

Abstract: According to an aspect, a computer-implemented method includes accessing a profile of a user that indicates a likelihood that the user will execute each of a plurality of types of processing when training a new AI model. A runtime matrix that includes identifiers of runtime environments is accessed. The matrix also includes, for each of the runtime environments, a frequency of use of the runtime environment to train previously trained AI models using each of the plurality of types of processing. One or more of the runtime environments is selected for output to the user based at least in part on the profile of the user and the runtime matrix. Identifiers of the selected one or more of the runtime environments are output to a user interface of the user along with a suggestion to use one of the selected one or more of the runtime environments.

Abstract: A method of operating a shared computing environment includes receiving a client request for an optimized runtime configuration of the shared computing environment for executing a task, receiving runtime data of the task collected from a prior runtime, inferring a runtime configuration from the runtime data, determining a predicted timeseries workload for the shared computing environment, updating the runtime configuration to including the timeseries workload prediction, wherein the updating outputs the optimized runtime configuration, and configuring the shared computing environment according to the optimized runtime configuration in executing the task.

Abstract: A method of operating a shared computing environment includes receiving a client request for an optimized runtime configuration of the shared computing environment for executing a task, receiving runtime data of the task collected from a prior runtime, inferring a runtime configuration from the runtime data, determining a predicted timeseries workload for the shared computing environment, updating the runtime configuration to including the timeseries workload prediction, wherein the updating outputs the optimized runtime configuration, and configuring the shared computing environment according to the optimized runtime configuration in executing the task.

Abstract: Systems and methods for computer-automated labeling of data are disclosed. In embodiments, a method includes: identifying technical data assets in lineage data and corresponding business items in User Interface (UI) data of a user, wherein the lineage data includes a data source for the UI data; mapping the technical data assets to the corresponding business items; determining relevant labels to assign to the technical data assets from a label repository based on a similarity analysis of names of the corresponding business items and labels in the label repository; determining that one or more of the relevant labels meet a confidence threshold based on the similarity analysis; and automatically assigning the one or more of the relevant labels to associated ones of the technical data assets based on the determining that the one or more of the relevant labels meet the confidence threshold.

Abstract: A computer-implemented method, system and computer program product for identifying an appropriate contact across collaborative applications. Contact information is collected from monitored messages, communication lists and contact lists in each collaborative application. Contact records are generated based on the collected contact information, where such records are inserted into a contact list. After receiving a query from a user containing a keyword(s) that include a nickname (or portion thereof) of a second user whom the user desires to interact via the current collaborative application, a search is performed in the contact list for any record containing a nickname that is similar to the provided keyword(s). Record(s) in the contact list containing a nickname that exceeds a threshold degree of similarity as the keyword(s) are identified.

Abstract: Systems and methods for computer-automated labeling of data are disclosed. In embodiments, a method includes: identifying technical data assets in lineage data and corresponding business items in User Interface (UI) data of a user, wherein the lineage data includes a data source for the UI data; mapping the technical data assets to the corresponding business items; determining relevant labels to assign to the technical data assets from a label repository based on a similarity analysis of names of the corresponding business items and labels in the label repository; determining that one or more of the relevant labels meet a confidence threshold based on the similarity analysis; and automatically assigning the one or more of the relevant labels to associated ones of the technical data assets based on the determining that the one or more of the relevant labels meet the confidence threshold.

Abstract: A computer-implemented method, system and computer program product for identifying an appropriate contact across collaborative applications. Contact information is collected from monitored messages, communication lists and contact lists in each collaborative application. Contact records are generated based on the collected contact information, where such records are inserted into a contact list. After receiving a query from a user containing a keyword(s) that include a nickname (or portion thereof) of a second user whom the user desires to interact via the current collaborative application, a search is performed in the contact list for any record containing a nickname that is similar to the provided keyword(s). Record(s) in the contact list containing a nickname that exceeds a threshold degree of similarity as the keyword(s) are identified.

Abstract: An application store system and an application development method using the application store system are provided in the disclosure.

Abstract: An application store system and an application development method using the application store system are provided in the disclosure.