Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: In some implementations, a computing system can perform crowd sourced access point mapping using privacy preserving techniques. For example, a server device can receive access point data reports from multiple user devices that includes various attributes and/or measurements with respect to access points with which the user devices have established connections. The access point data reports can be delivered without identifying the user devices, or users thereof, and their exact locations. Based on the access point data reports, the server device can map locations of access points and relationships between access points to generate network topologies, network locations, network quality scores, and various other characteristics of networks and/or access points. The generated access point data can be served to user devices so that the user devices can improve the efficiency of the interactions between user devices and the detected access points and/or networks.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: A location control subsystem is provided that allows a user of an electronic device to define the granularity used to provide location coarseness. A user can define a coarse location granularity for an application. When a coarse device location is reported to an application, the location can be provided with at least a minimum degree of variable specificity based on the selected location coarseness. When an application is granted a coarse location, the application is to interpret the provided location indicating that the user may be anywhere within a geographic region of variable specificity, as opposed to being close to a center point with a horizontal accuracy based on the precision of the location fix, as when a fine granularity location is provided. In addition to reducing the spatial resolution of the location that is reported to the application, the temporal resolution may also be reduced.

Abstract: In some implementations, a computing system can perform crowd sourced access point mapping using privacy preserving techniques. For example, a server device can receive access point data reports from multiple user devices that includes various attributes and/or measurements with respect to access points with which the user devices have established connections. The access point data reports can be delivered without identifying the user devices, or users thereof, and their exact locations. Based on the access point data reports, the server device can map locations of access points and relationships between access points to generate network topologies, network locations, network quality scores, and various other characteristics of networks and/or access points. The generated access point data can be served to user devices so that the user devices can improve the efficiency of the interactions between user devices and the detected access points and/or networks.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: Techniques for selective sharing of device location are described. An electronic user device may reference a location index generated by a service provider as part of deciding whether to share its device location. The location index may ensure that the electronic user device shares relevant location information, while refraining from sharing location information of a sensitive or private nature.

Abstract: A distributed offline indexing system uses a set of data processing systems in a distributed computing environment to create an index database that can store, for example, data about geographical or geospatial areas. The index database can be distributed across a plurality of database shards such that each shard includes an index file and a DB file. The index files include keys that refer to values in their corresponding DB files. The keys are used to look-up their corresponding values at search time. At indexing time, the keys are hashed, with an entropy creating hash, to distribute the keys across the shards.

Abstract: In some implementations, a computing system can perform crowd sourced access point mapping using privacy preserving techniques. For example, a server device can receive access point data reports from multiple user devices that includes various attributes and/or measurements with respect to access points with which the user devices have established connections. The access point data reports can be delivered without identifying the user devices, or users thereof, and their exact locations. Based on the access point data reports, the server device can map locations of access points and relationships between access points to generate network topologies, network locations, network quality scores, and various other characteristics of networks and/or access points. The generated access point data can be served to user devices so that the user devices can improve the efficiency of the interactions between user devices and the detected access points and/or networks.

Abstract: In some implementations, a computing system can perform crowd sourced access point mapping using privacy preserving techniques. For example, a server device can receive access point data reports from multiple user devices that includes various attributes and/or measurements with respect to access points with which the user devices have established connections. The access point data reports can be delivered without identifying the user devices, or users thereof, and their exact locations. Based on the access point data reports, the server device can map locations of access points and relationships between access points to generate network topologies, network locations, network quality scores, and various other characteristics of networks and/or access points. The generated access point data can be served to user devices so that the user devices can improve the efficiency of the interactions between user devices and the detected access points and/or networks.

Abstract: In some implementations, a computing system can perform crowd sourced access point mapping using privacy preserving techniques. For example, a server device can receive access point data reports from multiple user devices that includes various attributes and/or measurements with respect to access points with which the user devices have established connections. The access point data reports can be delivered without identifying the user devices, or users thereof, and their exact locations. Based on the access point data reports, the server device can map locations of access points and relationships between access points to generate network topologies, network locations, network quality scores, and various other characteristics of networks and/or access points. The generated access point data can be served to user devices so that the user devices can improve the efficiency of the interactions between user devices and the detected access points and/or networks.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for disambiguating clustered occurrence identifiers. Digital assets contain a plurality of metadata that can be used to identify the occurrence or event during which the digital asset was captured. Techniques can use various types contextual information based on a category of the digital asset for disambiguation. Events can occur at various locations, over a dispersed area and it can be challenging to automatically identify and label the digital assets with the identification of the event. Automatically labelling the digital assets assists user in organizing and sharing the digital assets with friends and family. Users can search for events by event name, performer name, and category.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for disambiguating clustered occurrence identifiers. Digital assets contain a plurality of metadata that can be used to identify the occurrence or event during which the digital asset was captured. Techniques can use various types contextual information based on a category of the digital asset for disambiguation. Events can occur at various locations, over a dispersed area and it can be challenging to automatically identify and label the digital assets with the identification of the event. Automatically labelling the digital assets assists user in organizing and sharing the digital assets with friends and family. Users can search for events by event name, performer name, and category.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.