Abstract: Disclosed herein are systems, products, and/or methods for determining a dependency between a task and a hardware component executing the task. The method may include: accessing an execution log storing information related to a plurality of tasks, each task of the plurality of tasks being executed by a respective computing device of a plurality of computing devices distributed across a network architecture; identifying a task of the plurality of tasks to obtain application layer information of the identified task; determining which respective computing device executed the identified task to obtain network layer information of the respective computing device; generating a dependency map illustrating a relationship between the identified task and the respective computing device that executed the identified task, the relationship including the application layer information and the network layer information; and displaying, using an interactive graphical user interface (GUI) on a user device, the dependency map.

Abstract: Disclosed herein are systems, products, and/or methods for determining a dependency between a task and a hardware component executing the task. The method may include: accessing an execution log storing information related to a plurality of tasks, each task of the plurality of tasks being executed by a respective computing device of a plurality of computing devices distributed across a network architecture; identifying a task of the plurality of tasks to obtain application layer information of the identified task; determining which respective computing device executed the identified task to obtain network layer information of the respective computing device; generating a dependency map illustrating a relationship between the identified task and the respective computing device that executed the identified task, the relationship including the application layer information and the network layer information; and displaying, using an interactive graphical user interface (GUI) on a user device, the dependency map.

Abstract: Disclosed herein are systems, products, and/or methods for determining a dependency between a task and a hardware component executing the task. The method may include: accessing an execution log storing information related to a plurality of tasks, each task of the plurality of tasks being executed by a respective computing device of a plurality of computing devices distributed across a network architecture; identifying a task of the plurality of tasks to obtain application layer information of the identified task; determining which respective computing device executed the identified task to obtain network layer information of the respective computing device; generating a dependency map illustrating a relationship between the identified task and the respective computing device that executed the identified task, the relationship including the application layer information and the network layer information; and displaying, using an interactive graphical user interface (GUI) on a user device, the dependency map.

Abstract: Disclosed herein are systems, products, and/or methods for determining a dependency between a task and a hardware component executing the task. The method may include: accessing an execution log storing information related to a plurality of tasks, each task of the plurality of tasks being executed by a respective computing device of a plurality of computing devices distributed across a network architecture; identifying a task of the plurality of tasks to obtain application layer information of the identified task; determining which respective computing device executed the identified task to obtain network layer information of the respective computing device; generating a dependency map illustrating a relationship between the identified task and the respective computing device that executed the identified task, the relationship including the application layer information and the network layer information; and displaying, using an interactive graphical user interface (GUI) on a user device, the dependency map.

Abstract: Systems and methods for selecting a runway record for a target runway are provided. The system includes: a first database having a first runway record with a first runway threshold; a second database having a second runway record with a second runway threshold; a transceiver for receiving an ILS signal; and a computer system. The computer system is configured to determine when the ILS signal is substantially zero. When the ILS signal is substantially zero, the computer system is configured to receive, from one or more databases, a plurality of runway records including corresponding runway thresholds; and select a most accurate runway record from the plurality of runway records based, at least in part, on which of the corresponding runway thresholds deviates least from the aiming point. In various embodiments, the most accurate runway record is then utilized for rendering images in a cockpit display system.

Abstract: A device may analyze a set of unstructured documents of an organization associated with a domain to identify a first set of entities. The device may analyze a set of semi-structured documents of the organization to determine a second set of entities. The device may filter the first set of entities using the second set of entities. Filtering the first set of entities may include removing, from the first set of entities, one or more entities that do not satisfy a threshold level of similarity with entities included in the second set of entities. The device may consolidate the filtered first set of entities and the second set of entities to identify a set of key entities. The device may provide the set of key entities to a user device to allow the set of key entities to be annotated and used for one or more machine learning models.

Abstract: Systems and methods for selecting a runway record for a target runway are provided. The system includes: a first database having a first runway record with a first runway threshold; a second database having a second runway record with a second runway threshold; a transceiver for receiving an ILS signal; and a computer system. The computer system is configured to determine when the ILS signal is substantially zero. When the ILS signal is substantially zero, the computer system is configured to receive, from one or more databases, a plurality of runway records including corresponding runway thresholds; and select a most accurate runway record from the plurality of runway records based, at least in part, on which of the corresponding runway thresholds deviates least from the aiming point. In various embodiments, the most accurate runway record is then utilized for rendering images in a cockpit display system.

Abstract: A device may analyze a set of unstructured documents of an organization associated with a domain to identify a first set of entities. The device may analyze a set of semi-structured documents of the organization to determine a second set of entities. The device may filter the first set of entities using the second set of entities. Filtering the first set of entities may include removing, from the first set of entities, one or more entities that do not satisfy a threshold level of similarity with entities included in the second set of entities. The device may consolidate the filtered first set of entities and the second set of entities to identify a set of key entities. The device may provide the set of key entities to a user device to allow the set of key entities to be annotated and used for one or more machine learning models.

Abstract: A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.

Abstract: A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.