Abstract: Systems, computer-implemented methods, and computer program products that can facilitate calibrating a user activity model of a user device nodes are described. According to an embodiment, a method for calibrating a user activity model used by a mobile device can comprise receiving sensor data from a sensor of the mobile device. Further, applying a first weight to a first a first likelihood of a first occurrence of a first activity, wherein the first likelihood is determined by a first estimator of the user activity model by applying preconfigured criteria to the sensor data. The method can further comprise performing an action based on a determination of the first occurrence of the first activity, the determination being based on the first weight and the first likelihood of the first occurrence of the first activity.

Abstract: Some techniques described herein relate to determining how to optimally store datasets in a multi-tiered storage device with compression. In one example, a method includes assigning, to a data partition of a dataset, a priority based on access patterns of the data partition. Compression data is accessed describing results of compressing a data sample associated with the data partition using multiple compression schemes. Based both on the priority of the data partition and the compression data, a storage tier is determined for storing the data partition in the multi-tiered storage device. Further, based both on the priority of the data partition and the compression data, a compression scheme is determined for compressing the data partition for storage in the multi-tiered storage device. The data partition is compressed using the compression scheme to produce a compressed data partition, and the compressed data partition is stored in the storage tier.

Abstract: Some techniques described herein relate to determining how to optimally store datasets in a multi-tiered storage device with compression. In one example, a method includes assigning, to a data partition of a dataset, a priority based on access patterns of the data partition. Compression data is accessed describing results of compressing a data sample associated with the data partition using multiple compression schemes. Based both on the priority of the data partition and the compression data, a storage tier is determined for storing the data partition in the multi-tiered storage device. Further, based both on the priority of the data partition and the compression data, a compression scheme is determined for compressing the data partition for storage in the multi-tiered storage device. The data partition is compressed using the compression scheme to produce a compressed data partition, and the compressed data partition is stored in the storage tier.

Abstract: Disclosed are apparatus and methods for automatically training a sensor node to detect anomalies in an environment. An indication is sent to the sensor node to initiate training by the sensor node to detect anomalies in object(s) in the environment based on sensor data generated by a sensor operable to detect signals from the one or more objects in the environment. After training is initiated, the sensor node automatically trains a model in communication with the sensor to detect anomalies in the one or more objects in the environment, wherein such training is based on the sensor data. After the model is trained, the model to detect anomalies in the object(s) in the environment is executed by the sensor node.

Abstract: Disclosed are apparatus and methods for automatically training a sensor node to detect anomalies in an environment. At the sensor node, an indication is received to initiate training by the sensor node to detect anomalies in the environment based on sensor data generated by a sensor that resides on such sensor node and is operable to detect sensor signals from the environment. After training is initiated, the sensor node automatically trains a model that resides on the sensor to detect anomalies in the environment, and such training is based on the sensor data. After the model is trained, the model to detect anomalies in the environment is executed by the sensor node.

Abstract: Disclosed are apparatus and methods for automatically training a sensor node to detect anomalies in an environment. At the sensor node, an indication is received to initiate training by the sensor node to detect anomalies in the environment based on sensor data generated by a sensor that resides on such sensor node and is operable to detect sensor signals from the environment. After training is initiated, the sensor node automatically trains a model that resides on the sensor to detect anomalies in the environment, and such training is based on the sensor data. After the model is trained, the model to detect anomalies in the environment is executed by the sensor node.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate calibrating a user activity model of a user device nodes are described. According to an embodiment, a method for calibrating a user activity model used by a mobile device can comprise receiving sensor data from a sensor of the mobile device. Further, applying a first weight to a first a first likelihood of a first occurrence of a first activity, wherein the first likelihood is determined by a first estimator of the user activity model by applying preconfigured criteria to the sensor data.