Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.

Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.

Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.

Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.

Abstract: Aspects of the subject disclosure may include, for example, obtaining roaming agreement data related to roaming agreements that are between a wireless provider and a respective one of a plurality of wireless roaming providers; obtaining, for each wireless subscriber of the wireless provider, respective roaming usage data, all of the respective roaming usage data comprising collective roaming usage data; training, based upon the collective roaming usage data, a set of one or more models, the one or more models comprising one or more statistical models, one or more machine learning models, or any combination thereof, the one or more models being trained with multiple iterations of feedback loops, and the training resulting in one or more trained models; estimating for each wireless subscriber, based upon the one or more trained models, respective projected location information for a future time, all of the respective projected location information comprising collective projected location information; obtaining,

Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.

Abstract: Disclosed are systems, methods and computer-readable media for identifying failed points in a network in real time. The system and method employ a topology database against which parsed and enhanced fault notifications are compared to identify the location of the fault notifications. The fault notifications are associated into a single event. A root cause analysis module having machine learning capabilities is used to match the single event with a predicted root cause by accessing a root cause database established with existing historic data and heuristically derived failure scenarios.

Abstract: One or more simulations are generated for in-home monitoring. The simulations model sensory detection of a user's physical activities using a different number and/or a different combination of sensors. Each different simulation may thus be associated with an accuracy and a cost, depending on the number and/or combination of sensors. The simulations thus present a range of sensory configurations that balance accuracy and affordability, from which an optimum sensory solution may be determined for the in-home monitoring.