Abstract: An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.

Abstract: An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.

Abstract: Example methods, apparatus and articles of manufacture (e.g., physical storage media) to perform real-time cost management for utilities are disclosed. Disclosed example methods for real-time utility cost management include displaying, on a display associated with an appliance, utility pricing data received via a network. Such disclosed example methods also include, after displaying the utility pricing data, determining, based on a first input received via a user interface associated with the appliance, whether remote control of the appliance is permitted. Such disclosed example methods further include, in response to determining that remote control of the appliance is permitted, delaying activation of an operation of the appliance until receipt of a first command via the network.

Abstract: A mechanism is described for facilitating location-based zone management for computing systems according to one embodiment. A method of embodiments, as described herein, includes receiving, at a server computing device, a request to locate a destination relating to an entity, retrieving a first data relating to an outdoor navigation portion of an address relating to the destination, and retrieving a second data relating to an indoor navigation portion of the address relating to the destination. The first and second data are retrieved from a first database coupled with the first server computing device. The method may further include communicating the first data and the second data to a client computing device.

Abstract: A mechanism is described for facilitating proximity and context-based deduction of global positioning of computing devices according to one embodiment of the invention. A method of embodiments of the invention includes detecting wireless proximity of a plurality of computing devices with respect to a computing device, and selecting one or more of the plurality of computing devices based on their detected proximity to the computing device. The method may further include receiving contextual information from the one or more computing devices, and semantically deducing location of the computing device based on the received contextual information.

Abstract: A mechanism is described for facilitating proximity and context-based deduction of global positioning of computing devices according to one embodiment of the invention. A method of embodiments of the invention includes detecting wireless proximity of a plurality of computing devices with respect to a computing device, and selecting one or more of the plurality of computing devices based on their detected proximity to the computing device. The method may further include receiving contextual information from the one or more computing devices, and semantically deducing location of the computing device based on the received contextual information.