Abstract: Various techniques are used to schedule computing jobs for execution by a computing resource. In an example method, a schedule is generated by selecting, for a first slot in the schedule, a first computing job based on a first priority of the first computing job with respect to a first characteristic. A second computing job is selected for a second slot in the schedule based on a second priority of the second computing job with respect to a second characteristic. The second slot occurs after the first slot in the schedule, and the second characteristic is different than the first characteristic. The first characteristic or the second characteristic includes an execution frequency. The computing jobs are executed based on the schedule.

Abstract: The present invention realtes to a process for the detoxification and concentration of xylose rich biomass derived liquid hydrolysate and its application as a substrate for the synthesis of crystalline and pure xylitol.

Abstract: Various techniques are used to schedule computing jobs for execution by a computing resource. In an example method, a schedule is generated by selecting, for a first slot in the schedule, a first computing job based on a first priority of the first computing job with respect to a first characteristic. A second computing job is selected for a second slot in the schedule based on a second priority of the second computing job with respect to a second characteristic. The second slot occurs after the first slot in the schedule, and the second characteristic is different than the first characteristic. The first characteristic or the second characteristic includes an execution frequency. The computing jobs are executed based on the schedule.

Abstract: Various techniques are used to schedule computing jobs for execution by a computing resource. In an example method, a schedule is generated by selecting, for a first slot in the schedule, a first computing job based on a first priority of the first computing job with respect to a first characteristic. A second computing job is selected for a second slot in the schedule based on a second priority of the second computing job with respect to a second characteristic. The second slot occurs after the first slot in the schedule, and the second characteristic is different than the first characteristic. The first characteristic or the second characteristic includes an execution frequency. The computing jobs are executed based on the schedule.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system identifies content shared within an online system and interactions between users of the online system and the content that match behavioral criteria and content-based criteria in a set of rules. Next, the system aggregates the content and the interactions into trends in the content and the interactions, predictions associated with the content and the interactions, and recommendations for moderating the content and the interactions. The system then outputs representations of the trends, the predictions, and the recommendations in a user interface. Finally, the system receives, via the user interface, an action to be performed on a subset of the content and updates sharing of the content within the online system based on the action.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system identifies content shared within an online system and interactions between users of the online system and the content that match behavioral criteria and content-based criteria in a set of rules. Next, the system aggregates the content and the interactions into trends in the content and the interactions, predictions associated with the content and the interactions, and recommendations for moderating the content and the interactions. The system then outputs representations of the trends, the predictions, and the recommendations in a user interface. Finally, the system receives, via the user interface, an action to be performed on a subset of the content and updates sharing of the content within the online system based on the action.

Abstract: Various techniques are used to schedule computing jobs for execution by a computing resource. In an example method, a schedule is generated by selecting, for a first slot in the schedule, a first computing job based on a first priority of the first computing job with respect to a first characteristic. A second computing job is selected for a second slot in the schedule based on a second priority of the second computing job with respect to a second characteristic. The second slot occurs after the first slot in the schedule, and the second characteristic is different than the first characteristic. The first characteristic or the second characteristic includes an execution frequency. The computing jobs are executed based on the schedule.

Abstract: In an embodiment, one or more non-transitory computer-readable storage media store one or more sequences of instructions, which when executed using one or more processors causes the one or more processors to perform various functions, such as accessing data stored in memory regarding a plurality of computation jobs. Such data includes, for instance, periodicity constraints that specify execution frequencies for the plurality of computation jobs, categorization data that categorizes the plurality of computation jobs into a plurality of job types, and organization data that organizes the plurality of computation jobs into a plurality of ordered arrangements. In this embodiment, there are at least a first ordered arrangement and a second ordered arrangement, each ordered arrangement comprises references to each of the plurality of computation jobs according to job type, and the job types are associated with relative priority indicia.

Abstract: In one embodiment, a system includes: a plurality of cloud nodes implemented on computing devices, the plurality of cloud nodes configured to execute computing jobs in a cloud computing environment according to a schedule, a schedule optimizer configured to: use a machine learning model to determine functional intent for job requests according to at least job execution metadata, and generate a schedule recommendation for jobs associated with the job requests, where the schedule recommendation is generated based at least in part on the functional intent; and a job executor configured to provide the schedule recommendation as the schedule to the plurality of cloud nodes, where the schedule optimizer and the job executor are instantiated in memory and executed by processing circuitry on at least one computing device.

Abstract: In an embodiment, one or more non-transitory computer-readable storage media store one or more sequences of instructions, which when executed using one or more processors causes the one or more processors to perform various functions, such as accessing data stored in memory regarding a plurality of computation jobs. Such data includes, for instance, periodicity constraints that specify execution frequencies for the plurality of computation jobs, categorization data that categorizes the plurality of computation jobs into a plurality of job types, and organization data that organizes the plurality of computation jobs into a plurality of ordered arrangements. In this embodiment, there are at least a first ordered arrangement and a second ordered arrangement, each ordered arrangement comprises references to each of the plurality of computation jobs according to job type, and the job types are associated with relative priority indicia.

Abstract: In one embodiment, a system includes: a plurality of cloud nodes implemented on computing devices, the plurality of cloud nodes configured to execute computing jobs in a cloud computing environment according to a schedule, a schedule optimizer configured to: use a machine learning model to determine functional intent for job requests according to at least job execution metadata, and generate a schedule recommendation for jobs associated with the job requests, where the schedule recommendation is generated based at least in part on the functional intent; and a job executor configured to provide the schedule recommendation as the schedule to the plurality of cloud nodes, where the schedule optimizer and the job executor are instantiated in memory and executed by processing circuitry on at least one computing device.

Abstract: Systems and methods for biometrically identifying an individual for purchasing a product and/or service without a need for the individual to input authentication data into a system/device are described. Data representative of a scanned iris of a customer desiring to make a purchase may be made. The customer may be identified based upon the scanned iris. A determination may be made as to whether the identified customer has a plurality of accounts at a financial entity. Information of a selected account to make the purchase may be received. Sufficient funds in the selected account to make the purchase may be determined. If insufficient, a different account may be identified to make the purchase. Sufficient funds in the different accounts may be determined and funds in the different accounts may be decreased by the associated payment amount.

Abstract: Techniques for bridging data from business support units, e.g., call centers and marketing operations, with a data warehouse to augment and enrich pre-existing customer information. Unstructured data is received with incomplete integration information. A data key is created from the unstructured data, and the unstructured data is integrated with the structured data in a data warehouse based on the data key. Unstructured data can assume different forms of data, including recorded audio data, facial image data, and iris image data. At least one customer identifier is extracted from the unstructured data by data mining. A data key is subsequently created from the at least one customer identifier. Incomplete integration may include partial integration information or may not include any integration information.