Abstract: Computer-readable media, methods, and systems are disclosed for scheduling a start time and a shutdown time of one or more online resources associated with a multi-cloud resource scheduler. A request from a first user is received to access a multi-cloud resource scheduler associated with one or more online resources. Responsive to the request from the first user, credentials of the first user are validated prior to providing access to the multi-cloud resource scheduler. Based upon validating the credentials of the first user, access to the multi-cloud resource scheduler is provided. Instructions are received from the first user to schedule a start time and a shutdown time of at least one online cloud resource connected to the multi-cloud resource scheduler. An availability of the at least one online cloud resource is established for access by a second user based on the instructions.

Abstract: Some embodiments are directed to a federated machine learning, including the inference and training. Inference may be done by applying multiple machine learnable models to a mapped record. The mapped record may be obtained by applying a mapping rule to a local record. The mapping rule may generalize or extend data features in the local record.

Abstract: Computer-readable media, methods, and systems are disclosed for storing and analyzing dynamic data within a semantic data store. The dynamic data comprises one or more types of data having a normalized data schema. A dynamic data manager interfaces with the semantic data store to instruct storage of the data. The data may be received through an event service from either of an external data source or an internal data source.

Abstract: Techniques for implementing a digital assistant that provides proactive notifications to users, summarizes data and relevant situations, forecasts/predicts future outcomes, simulates outcomes under different assumptions, generates recommendations to improve observed or assumed situations, and provides explanations for calculated outcomes are disclosed. In some example embodiments, a computer system is configured to detect a data change in one or more data sources, the data change corresponding to a monitored data object, generate a predicted future value for the monitored data object based on the detected data change, identify a deviation between the predicted future value and a planned future value for the monitored data object, determine that the identified deviation is relevant for a specific user at a specific time and in a specific context, and cause a notification corresponding to the deviation to be displayed on a computing device based on the determination that the deviation is relevant.

Abstract: In an example embodiment a market data management system allows users to track information regarding commodities. A representative market data management system comprises physical commodity objects including a plurality of fields that describe a real world commodity, market identifier objects including market identifier codes, and contract specification objects including a plurality of fields that describe a derivative contract specification for a particular commodity traded at a particular commodity market. In the data model, a one to many relationship may exist for a physical commodity object and contract specification objects (e.g., a given commodity may be traded according to different derivative contract specifications). The data model may also allow a many to many relationship between contract specification objects and market identifier objects (e.g.

Abstract: A system receives a commodity identification, a curve type, and a curve category. The system also receives an interpolation identification, an extrapolation identification, a read procedure, and a maximum number of days for a readback. The system further receives contract data, the contract data including a market identifier code, a derivative contract specification (DCS) identification, and a price type. The system uses the contract data to generate a commodity curve based on DCS, and displays the commodity curve based on DCS on an electronic display unit.