Abstract: Systems and methods for establishing relationships between building automation system components and controlling building automation system components. Data for a building automation system components may be received from the building automation system components and one or more models may be applied to the received data to determine types of the building automation system components and relationships between building automation system components. Once the types of building automation system components have been determined or identified, uniform names may be applied to the building automation system components. The received data may include, among other data, naming data and telemetry data from the building automation system components.

Abstract: Systems and methods for establishing relationships between building automation system components and controlling building automation system components. Data for a building automation system components may be received from the building automation system components and one or more models may be applied to the received data to determine types of the building automation system components and relationships between building automation system components. Once the types of building automation system components have been determined or identified, uniform names may be applied to the building automation system components. The received data may include, among other data, naming data and telemetry data from the building automation system components.

Abstract: A system includes a computer processor located within a cloud system. The system receives data from a sensor associated with an industrial or home automation control application. The sensor is configured to monitor a first condition of a device. The computer processor analyzes the data, and transmits a software update or software download from the cloud system to the industrial or home automation control application based on the data analysis.

Abstract: Embodiments relate generally to a system having one or more computerized kiosks (102, 150, 160, 180, 192) operable to associate a personal protection device (104) to a worker's personal information. In some embodiments, the kiosk (102, 150, 160, 180, 192) can use the worker's personal information to custom configure the personal protection device (104), so that the device (104) will better suit the needs of the specific worker.

Abstract: Embodiments relate generally to a system having one or more computerized kiosks (102, 150, 160, 180, 192) operable to associate a personal protection device (104) to a worker's personal information. In some embodiments, the kiosk (102, 150, 160, 180, 192) can use the worker's personal information to custom configure the personal protection device (104), so that the device (104) will better suit the needs of the specific worker.

Abstract: A system includes a computer processor located within a cloud system. The system receives data from a sensor associated with an industrial or home automation control application. The sensor is configured to monitor a first condition of a device. The computer processor analyzes the data, and transmits a software update or software download from the cloud system to the industrial or home automation control application based on the data analysis.

Abstract: Devices, methods, and systems for security log mining are described herein. One method includes combining, using a data fusion unit of an access control system, features of structured and non-structured data associated with system access events for a number of users into a combined data set, generating, using an anomaly detection engine of the access control system, a model of behavior for the number of users based on the combined data set, and comparing, using the anomaly detection engine of the access control system, real time behavior for the number of users to the model for the number of users to determine whether the real time behavior for the number of users is anomalous behavior for the number of users.

Abstract: Devices, methods, and systems for security log mining are described herein. One method includes combining, using a data fusion unit of an access control system, features of structured and non-structured data associated with system access events for a number of users into a combined data set, generating, using an anomaly detection engine of the access control system, a model of behavior for the number of users based on the combined data set, and comparing, using the anomaly detection engine of the access control system, real time behavior for the number of users to the model for the number of users to determine whether the real time behavior for the number of users is anomalous behavior for the number of users.