Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for the generation of a recommendation for one or more resource transformation actions to be performed based at least in part on an optimized resource transformation scenario. The optimized resource transformation scenario can be identified based at least in part on a hybrid resource transformation scenario that can be based at least in part on a resource priority score for a residual resource and a downgrade-only resource transformation scenario. The downgrade set of a plurality of resources can be determined based at least in part on resource transformation data associated with the plurality of resources.

Abstract: In general, this disclosure describes techniques for automatically restructuring a database to improve one or more parameters of the database. In some examples, a computing system is configured to extract a set of columns and merge the set of columns into a new table of a first new candidate model of the database; determine a table of the database based on a number of columns of that are involved in query “where” or “join” clauses; merge the table with one or more connected tables in a second new candidate model of the database, wherein the one or more connected tables are connected to the table by at least one of the “where” or “join” clauses; select a model of the database from among the candidate models based on one or more parameters; and use the selected model as the current model of the database.

Abstract: A computing device includes a memory and processing circuitry. The memory is configured to store an organizational proximity dataset for a current user. The processing is configured to generate scores for a plurality of resources based on the organizational proximity dataset stored to the memory for the current user. The processing circuitry is further configured to recommend one or more resources of the plurality of resources to the current user based on the scores generated for the plurality of resources.

Abstract: There is a need to accurately and dynamically generate a time-based prediction and control access to the time-based prediction. This need can be addressed, for example, by allowing controlled access to user information pertaining to a user visiting a facility. In one example, a method includes storing a companion user identifier in association with a public user identifier; receiving a request for a user status of the user; determining whether the companion user is permitted to receive the user status of the user based at least in part on the public user identifier and the companion user identifier; responsive to determining that the companion user is permitted to receive the user status of the user, requesting an aggregated time-based prediction for the user; and causing, the aggregated time-based prediction to be decrypted and provided as a response to the request for the user status of the user.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for the generation of a recommendation for one or more resource transformation actions to be performed based at least in part on an optimized resource transformation scenario. The optimized resource transformation scenario can be identified based at least in part on a hybrid resource transformation scenario that can be based at least in part on a resource priority score for a residual resource and a downgrade-only resource transformation scenario. The downgrade set of a plurality of resources can be determined based at least in part on resource transformation data associated with the plurality of resources.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for optimized resource transformation given a set of resource optimization parameters.

Abstract: There is a need to accurately and dynamically generate a time-based prediction and control access to the time-based prediction. This need can be addressed, for example, by allowing controlled access to user information pertaining to a user visiting a facility. In one example, a method includes storing a companion user identifier in association with a public user identifier; receiving a request for a user status of the user; determining whether the companion user is permitted to receive the user status of the user based at least in part on the public user identifier and the companion user identifier; responsive to determining that the companion user is permitted to receive the user status of the user, requesting an aggregated time-based prediction for the user; and causing, the aggregated time-based prediction to be decrypted and provided as a response to the request for the user status of the user.

Abstract: There is a need to accurately and dynamically generate a time-based prediction and control access to the time-based prediction. This need can be addressed, for example, by allowing controlled access to user information pertaining to a user visiting a facility. In one example, a method includes storing a companion user identifier in association with a public user identifier; receiving a request for a user status of the user; determining whether the companion user is permitted to receive the user status of the user based at least in part on the public user identifier and the companion user identifier; responsive to determining that the companion user is permitted to receive the user status of the user, requesting an aggregated time-based prediction for the user; and causing, the aggregated time-based prediction to be decrypted and provided as a response to the request for the user status of the user.

Abstract: For each computing task of a plurality of computing tasks, a computing system is configured to apply a machine learning process that determines an expected usage time for output data associated with the computing task. Execution of the computing task generates the output data associated with the computing task. The expected usage time for the output data associated with the computing task is a time at which a user associated with the computing task can be expected to access the output data associated with the computing task. The computing system is configured to schedule execution of one or more of the computing tasks such that expected times of completion of the one or more computing tasks are prior to the expected usage times for the one or more computing tasks.

Abstract: In general, this disclosure describes techniques for automatically restructuring a database to improve one or more parameters of the database. In some examples, a computing system is configured to extract a set of columns and merge the set of columns into a new table of a first new candidate model of the database; determine a table of the database based on a number of columns of that are involved in query “where” or “join” clauses; merge the table with one or more connected tables in a second new candidate model of the database, wherein the one or more connected tables are connected to the table by at least one of the “where” or “join” clauses; select a model of the database from among the candidate models based on one or more parameters; and use the selected model as the current model of the database.

Abstract: Solutions for more efficient and effective traversal of infection networks are disclosed.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for optimized resource transformation given a set of resource optimization parameters.

Abstract: A computing device includes a memory and processing circuitry. The memory is configured to store an organizational proximity dataset for a current user. The processing is configured to generate scores for a plurality of resources based on the organizational proximity dataset stored to the memory for the current user. The processing circuitry is further configured to recommend one or more resources of the plurality of resources to the current user based on the scores generated for the plurality of resources.

Abstract: For each computing task of a plurality of computing tasks, a computing system is configured to apply a machine learning process that determines an expected usage time for output data associated with the computing task. Execution of the computing task generates the output data associated with the computing task. The expected usage time for the output data associated with the computing task is a time at which a user associated with the computing task can be expected to access the output data associated with the computing task. The computing system is configured to schedule execution of one or more of the computing tasks such that expected times of completion of the one or more computing tasks are prior to the expected usage times for the one or more computing tasks.