Embodiments are directed to data ingestion over a network. Raw data and integrated data associated with a plurality of separate data sources may be provided such that the raw data includes content associated with a plurality of subjects. Categorization models may be employed to categorize the raw data based on various features, such as, format, structure, data source, variability, volume, or associated entities. Matching models may be determined based on the categorization of the of the raw data, the integrated data and the content associated with the plurality of subjects. Matching models may generate a plurality of unified facts based on the raw data and the integrated data such that each unified fact is associated with a score associated with a quality of its match with a unified schema.

Exemplary embodiments provide a transportation staffing management system. An amount of transportations miles are forecasted for delivering inventory from a distribution enter to a store based on a sales forecast for a store. Non-driving time for drivers engaged in delivering inventory to a store is tracked based on data collected in real-time from mobile computing devices associated with delivery vehicles. An amount of time needed to deliver the inventory based on the forecasted amount of transportation miles and non-driving is calculated. An optimal transportation workload is generated for the distribution center based on the amount of time, data from a first database storing data from a central office, and data from a second database storing data from a distribution center.

An information processing apparatus includes an accumulation unit that accumulates history information regarding a flight history of an aircraft. A specification unit, based on the history information accumulated by the accumulation unit, specifies a candidate to be an operation assistant who assists an operation planned by an assisted operator. An output unit outputs information regarding the candidate to be the operation assistant specified by the specification unit.

A method and system for capturing research decision flow in a Research and Development (R&D) activity are provided. The method includes defining an objective of the R&D activity and determining one or more requirements for fulfilling the objective of the R&D activity. Further, the method includes identifying a set of risks associated with failure to satisfy the one or more requirements. Furthermore, the method also includes defining a set of tests to verify that the requirements are satisfied or the risks have been mitigated. Furthermore, the method also includes developing a task plan to satisfy the one or more requirements and mitigate one or more risks of the set of risks. Furthermore, the method also includes linking the objective, the one or more requirements, the set of risks and the task plan, to generate a linked information record.

A method and system for capturing research decision flow in a Research and Development (R&D) activity are provided. The method includes defining an objective of the R&D activity and determining one or more requirements for fulfilling the objective of the R&D activity. Further, the method includes identifying a set of risks associated with failure to satisfy the one or more requirements. Furthermore, the method also includes defining a set of tests to verify that the requirements are satisfied or the risks have been mitigated. Furthermore, the method also includes developing a task plan to satisfy the one or more requirements and mitigate one or more risks of the set of risks. Furthermore, the method also includes linking the objective, the one or more requirements, the set of risks and the task plan, to generate a linked information record.

An automated laboratory system for processing biological samples in a batch type manner is disclosed. In one embodiment, the system may receive assay instructions for biological samples processing among a plurality of devices. The devices may include a pre-analytical instrument and one or more analysis systems. The system may include an orchestration core application for determining an order of performance for the assays ordered for the samples.

According to the embodiments illustrated herein, a method is disclosed. The method comprises receiving, by a computing device comprising a processor, an input from an operator device, wherein the input facilitates determining a task to be performed. Further, the method comprises activating, by the computing device, a first light device associated with a first location in a workplace in response to receiving the input, wherein the first location is associated with the task to be performed. Furthermore, the method comprises receiving, by the computing device, a first voice input from the operator device, indicative of an exception encountered during execution of the task. Additionally, the method includes modifying the task, in response to determining that the first voice input is indicative of the exception encountered during execution of the task.

Systems and methods of the present disclosure leverage distributed ledger technology (DLT) to provide decentralized control of cooperative tasks performed by a plurality of robots. Characteristics of the plurality of robots may be stored in a distribute ledger, which may be provided by a blockchain or a distributed database system. When a service request is received, a set of tasks may be identified for providing the requested service and the robot characteristics recorded to the distributed ledger may be used to identify a list of candidate robots possessing characteristics corresponding to the set of tasks may be identified. A smart contract may be utilized to select one or more candidate robots for performing the task and to verify the selected robot(s) successfully completed the task. State information associated with operation of the selected robot(s) may be monitored to verify task completion.

A device may receive data that identifies applications utilized by users, databases utilized by the applications, and the users, and may process the received data, with first models, to determine context data that matches the users and events associated with the applications, and task data that identifies tasks to be performed by the users in response to the events. The device may process the received data and the context data, with a second model, to generate role data that identifies user interfaces utilized by the users to access the applications, and credentials of the users, and may process the context data, the task data, and the role data, with a third model, to generate persona data that identifies personas, and assignment data that assigns each of the users to one of the personas. The device may perform actions based on the persona data and the assignment data.

A vehicle system may include a memory of a vehicle configured to maintain at least one smart contract between a manufacturer of the vehicle and a supplier of components to the manufacturer, and a processor of the vehicle configured to receive at least one diagnostic code from the vehicle, the diagnostic code being associated with at least one vehicle component, in response to the code being identified by the processor as being associated with the at least one smart contract, update the at least one smart contract within the memory with the code, and transmit the smart contract, as updated, to a third party associated with the vehicle component.