Abstract: Arrangements for providing context aware response functions are provided. In some examples, sensor data may be received from a plurality of sensors in a sensor data farm associated with an enterprise organization. The sensor data may be received from various types of sensors and/or from sensors from various vendors or manufacturers. a machine learning model may be executed to analyze the sensor data. Based on an output of the machine learning model, a determination may be made as to whether one or more enterprise-specific thresholds have been met or exceeded. If not, additional, subsequently received data may be analyzed. If so, one or more notifications or notification actions may be identified. In some examples, the notification or notification actions may be executed and response data may be received. The response data may be used to update and/or validate the machine learning model.

Abstract: Arrangements for sensor data normalization and analysis are provided. In some arrangements, first sensor data may be received from a first sensor of a first sensor type. The first sensor may be associated with a first vendor and may output data in a first format unique to the first vendor. Second sensor data may be received from a second sensor. The second sensor may be a second sensor type. The second sensor may be associated with a second vendor and may output data in a second format unique to the second vendor. The first and second sensor data may be normalized from the first format and second format to a uniform format unique to an enterprise organization. The formatted data may be compared to one or more enterprise-specific thresholds. If one or more thresholds have been met or exceeded, a notification may be generated and transmitted to a computing device.

Abstract: Arrangements for providing context aware response functions are provided. In some examples, sensor data may be received from a plurality of sensors in a sensor data farm associated with an enterprise organization. The sensor data may be received from various types of sensors and/or from sensors from various vendors or manufacturers. a machine learning model may be executed to analyze the sensor data. Based on an output of the machine learning model, a determination may be made as to whether one or more enterprise-specific thresholds have been met or exceeded. If not, additional, subsequently received data may be analyzed. If so, one or more notifications or notification actions may be identified. In some examples, the notification or notification actions may be executed and response data may be received. The response data may be used to update and/or validate the machine learning model.

Abstract: Arrangements for sensor data normalization and analysis are provided. In some arrangements, first sensor data may be received from a first sensor of a first sensor type. The first sensor may be associated with a first vendor and may output data in a first format unique to the first vendor. Second sensor data may be received from a second sensor. The second sensor may be a second sensor type. The second sensor may be associated with a second vendor and may output data in a second format unique to the second vendor. The first and second sensor data may be normalized from the first format and second format to a uniform format unique to an enterprise organization. The formatted data may be compared to one or more enterprise-specific thresholds. If one or more thresholds have been met or exceeded, a notification may be generated and transmitted to a computing device.