Abstract: A job scheduling system determines a rate at which a user is providing user inputs to a user interface of a computing device. A set of jobs that is to be performed to display or otherwise present a current view of the user interface is identified in response to a user input. This set of jobs is modified by excluding from the set of jobs at least one job that is not estimated to run prior to the next user input. The user interface is displayed or otherwise presented as the modified set of jobs is performed.

Abstract: Techniques are disclosed for improving transfer speed for a plurality of files (e.g., image files) by using a Markov decision process to determine an optimal number of parallel instances of transfer stages and optimal file batch sizes for each instance. The transfer (e.g., import or export) operation involves different stages that are each optimized using the algorithm. The stages include a file fetch operation, a file processing operation, and a database update operation. Each of the stages may have multiple parallel instances to process many files at the same time. The Markov decision process uses a reward structure to determine the optimal number of parallel instances for each stage and the number of files operated on at each instance at any given moment in time. The process is dynamic and adaptable to any system environment since it does not rely on any particular hardware or operating system configuration.

Abstract: A context-based recommendation system for feature search automatically identifies features of a feature-rich system (e.g., an application) based on the program code of the feature-rich system and additional data corresponding to the feature-rich system. A code workflow graph describing workflows in the program code is generated. Various data corresponding to the feature-rich system, such as help data, analytics data, social media data, and so forth is obtained. The code workflow graph and the data are analyzed to identify sentences in the workflow. These sentences are used to a train machine learning system to generate one or more recommendations. In response to a user query, the machine learning system generates and outputs as recommendations workflows identified based on the user query.

Abstract: A job scheduling system determines a rate at which a user is providing user inputs to a user interface of a computing device. A set of jobs that is to be performed to display or otherwise present a current view of the user interface is identified in response to a user input. This set of jobs is modified by excluding from the set of jobs at least one job that is not estimated to run prior to the next user input. The user interface is displayed or otherwise presented as the modified set of jobs is performed.

Abstract: A job scheduling system determines a rate at which a user is providing user inputs to a user interface of a computing device. A set of jobs that is to be performed to display or otherwise present a current view of the user interface is identified in response to a user input. This set of jobs is modified by excluding from the set of jobs at least one job that is not estimated to run prior to the next user input. The user interface is displayed or otherwise presented as the modified set of jobs is performed.

Abstract: Examples may include a deduplication system threshold based on an amount of wear of a storage device. Examples may obtain an indication of an amount of wear experienced by at least one storage device storing a plurality of container indexes of a deduplication system, and may adjust a threshold of the deduplication system based on the amount of wear.

Abstract: In some embodiments, a key smoothener and predictor module of a software application executing on a computing device receives a sequence of key events from an input device of the computing device and through a user interface of the software application. The key smoothener and predictor module stores the sequence of key events in a key event queue and predicts the total number of key events for processing in a current processing cycle of the application based on the sequence of key events. A processing component of the software application processes an aggregated key event that indicates multiple keypresses. The number of the multiple keypresses is the same as the predicted total number of key events for the current processing cycle. The software application further causes the user interface of the software application to be updated based on processing the aggregated key event.

Abstract: In some embodiments, a key smoothener and predictor module of a software application executing on a computing device receives a sequence of key events from an input device of the computing device and through a user interface of the software application. The key smoothener and predictor module stores the sequence of key events in a key event queue and predicts the total number of key events for processing in a current processing cycle of the application based on the sequence of key events. A processing component of the software application processes an aggregated key event that indicates multiple keypresses. The number of the multiple keypresses is the same as the predicted total number of key events for the current processing cycle. The software application further causes the user interface of the software application to be updated based on processing the aggregated key event.

Abstract: A job scheduling system determines a rate at which a user is providing user inputs to a user interface of a computing device. A set of jobs that is to be performed to display or otherwise present a current view of the user interface is identified in response to a user input. This set of jobs is modified by excluding from the set of jobs at least one job that is not estimated to run prior to the next user input. The user interface is displayed or otherwise presented as the modified set of jobs is performed.

Abstract: Techniques are disclosed for improving transfer speed for a plurality of files (e.g., image files) by using a Markov decision process to determine an optimal number of parallel instances of transfer stages and optimal file batch sizes for each instance. The transfer (e.g., import or export) operation involves different stages that are each optimized using the algorithm. The stages include a file fetch operation, a file processing operation, and a database update operation. Each of the stages may have multiple parallel instances to process many files at the same time. The Markov decision process uses a reward structure to determine the optimal number of parallel instances for each stage and the number of files operated on at each instance at any given moment in time. The process is dynamic and adaptable to any system environment since it does not rely on any particular hardware or operating system configuration.

Abstract: A context-based recommendation system for feature search automatically identifies features of a feature-rich system (e.g., an application) based on the program code of the feature-rich system and additional data corresponding to the feature-rich system. A code workflow graph describing workflows in the program code is generated. Various data corresponding to the feature-rich system, such as help data, analytics data, social media data, and so forth is obtained. The code workflow graph and the data are analyzed to identify sentences in the workflow. These sentences are used to a train machine learning system to generate one or more recommendations. In response to a user query, the machine learning system generates and outputs as recommendations workflows identified based on the user query.

Abstract: Examples may include a deduplication system threshold based on an amount of wear of a storage device. Examples may obtain an indication of an amount of wear experienced by at least one storage device storing a plurality of container indexes of a deduplication system, and may adjust a threshold of the deduplication system based on the amount of wear.

Abstract: Examples may include a deduplication system threshold based on an amount of wear of a storage device. Examples may obtain an indication of an amount of wear experienced by at least one storage device storing a plurality of container indexes of a deduplication system, and may adjust a threshold of the deduplication system based on the amount of wear.