Abstract: Selection of content selection criteria based on entities related by relationship dimensions. In one aspect, a method receives a selection of a seed entity described in entity relation data, the entity relation data defining instances of entities, and for each entity one or more relationship dimensions; generating a set of selected entities; iteratively updating the set of selected entities, each iteration comprising: determining a set of relationship dimensions from the entities in the set of selected entities, each relationship dimension in the set being selected from the one or more relationship dimensions of the entities in the set of selected entities, receiving a selection of one of the relationship dimensions and in response: determining a set of candidate entities from the relationship dimensions and in response to receiving a selection of one or more candidate entities, updating the set of selected entities to include the one or more candidate entities.

Abstract: Selection of content selection criteria based on entities related by relationship dimensions. In one aspect, a method receives a selection of a seed entity described in entity relation data, the entity relation data defining instances of entities, and for each entity one or more relationship dimensions; generating a set of selected entities; iteratively updating the set of selected entities, each iteration comprising: determining a set of relationship dimensions from the entities in the set of selected entities, each relationship dimension in the set being selected from the one or more relationship dimensions of the entities in the set of selected entities, receiving a selection of one of the relationship dimensions and in response: determining a set of candidate entities from the relationship dimensions and in response to receiving a selection of one or more candidate entities, updating the set of selected entities to include the one or more candidate entities.

Abstract: Selection of content selection criteria based on entities related by relationship dimensions. In one aspect, a method receives a selection of a seed entity described in entity relation data, the entity relation data defining instances of entities, and for each entity one or more relationship dimensions; generating a set of selected entities; iteratively updating the set of selected entities, each iteration comprising: determining a set of relationship dimensions from the entities in the set of selected entities, each relationship dimension in the set being selected from the one or more relationship dimensions of the entities in the set of selected entities, receiving a selection of one of the relationship dimensions and in response: determining a set of candidate entities from the relationship dimensions and in response to receiving a selection of one or more candidate entities, updating the set of selected entities to include the one or more candidate entities.

Abstract: Selection of content selection criteria based on entities related by relationship dimensions. In one aspect, a method receives a selection of a seed entity described in entity relation data, the entity relation data defining instances of entities, and for each entity one or more relationship dimensions; generating a set of selected entities; iteratively updating the set of selected entities, each iteration comprising: determining a set of relationship dimensions from the entities in the set of selected entities, each relationship dimension in the set being selected from the one or more relationship dimensions of the entities in the set of selected entities, receiving a selection of one of the relationship dimensions and in response: determining a set of candidate entities from the relationship dimensions and in response to receiving a selection of one or more candidate entities, updating the set of selected entities to include the one or more candidate entities.

Abstract: Selection of content selection criteria based on entities related by relationship dimensions. In one aspect, a method receives a selection of a seed entity described in entity relation data, the entity relation data defining instances of entities, and for each entity one or more relationship dimensions; generating a set of selected entities; iteratively updating the set of selected entities, each iteration comprising: determining a set of relationship dimensions from the entities in the set of selected entities, each relationship dimension in the set being selected from the one or more relationship dimensions of the entities in the set of selected entities, receiving a selection of one of the relationship dimensions and in response: determining a set of candidate entities from the relationship dimensions and in response to receiving a selection of one or more candidate entities, updating the set of selected entities to include the one or more candidate entities.

Abstract: Selection of content selection criteria based on entities related by relationship dimensions. In one aspect, a method receives a selection of a seed entity described in entity relation data, the entity relation data defining instances of entities, and for each entity one or more relationship dimensions; generating a set of selected entities; iteratively updating the set of selected entities, each iteration comprising: determining a set of relationship dimensions from the entities in the set of selected entities, each relationship dimension in the set being selected from the one or more relationship dimensions of the entities in the set of selected entities, receiving a selection of one of the relationship dimensions and in response: determining a set of candidate entities from the relationship dimensions and in response to receiving a selection of one or more candidate entities, updating the set of selected entities to include the one or more candidate entities.

Abstract: Systems, methods, and machine-readable media for monitoring the locations of a number of entities are provided. The system may be configured to track the locations of a multitude of entities by grouping entities together into clusters of entities based on their geographic proximity and trajectories. A member of a cluster may be identified as a leader or a follower. The system may receive a number of location reports for the entities and update the locations of the entities based on whether the entity is a leader or a follower. For example, the system may update a back-end location database with the location information for a leader while location updates for followers may be converted into locations that are relative to the corresponding leader's and cached at the system.

Abstract: Techniques are described for obtaining high-resolution physical locations for a wireless device by leveraging the high-resolution physical location capabilities of wireless peers of the wireless device to provide a peer-to-peer location service and facilitate location targeting. Wireless devices provide location updates to a computing cloud, which stores records associating wireless device identifiers with received location information for the corresponding wireless devices. A wireless device issues a peer discovery request to dynamically identify nearby wireless peers in its network. The wireless device then sends wireless device identifiers for the nearby wireless peers in a location request to the computing cloud, which estimates a location for the wireless device using the previously recorded location information for the nearby wireless peers. The computing cloud returns the estimated location to the requesting wireless device.

Abstract: A device and method of determining a travel direction of a mobile device carried by a user are disclosed. The method includes determining a line along which the mobile device is traveling, where the determining may be based on sensed acceleration of the mobile device. A walking style of the user traveling with the mobile device may be determined. The sensed acceleration may be resolved into at least two mutually perpendicular components. A first component may be substantially parallel with gravity, and a second component may be orthogonal to the first component. The travel direction of the mobile device along the travel line may be determined based on a user's walking style and the resolved sensed acceleration. The determining of the line may include applying a linear least square fitting method to a plurality of acceleration readings obtained from at least one inertial navigation sensor.

Abstract: Techniques are described for obtaining high-resolution physical locations for a wireless device by leveraging the high-resolution physical location capabilities of wireless peers of the wireless device to provide a peer-to-peer location service and facilitate location targeting. Wireless devices provide location updates to a computing cloud, which stores records associating wireless device identifiers with received location information for the corresponding wireless devices. A wireless device issues a peer discovery request to dynamically identify nearby wireless peers in its network. The wireless device then sends wireless device identifiers for the nearby wireless peers in a location request to the computing cloud, which estimates a location for the wireless device using the previously recorded location information for the nearby wireless peers. The computing cloud returns the estimated location to the requesting wireless device.

Abstract: Techniques are described for obtaining high-resolution physical locations for a wireless device by leveraging the high-resolution physical location capabilities of wireless peers of the wireless device to provide a peer-to-peer location service and facilitate location targeting. Wireless devices provide location updates to a computing cloud, which stores records associating wireless device identifiers with received location information for the corresponding wireless devices. A wireless device issues a peer discovery request to dynamically identify nearby wireless peers in its network. The wireless device then sends wireless device identifiers for the nearby wireless peers in a location request to the computing cloud, which estimates a location for the wireless device using the previously recorded location information for the nearby wireless peers. The computing cloud returns the estimated location to the requesting wireless device.

Abstract: Techniques are described for obtaining high-resolution physical locations for a wireless device by leveraging the high-resolution physical location capabilities of wireless peers of the wireless device to provide a peer-to-peer location service and facilitate location targeting. Wireless devices provide location updates to a computing cloud, which stores records associating wireless device identifiers with received location information for the corresponding wireless devices. A wireless device issues a peer discovery request to dynamically identify nearby wireless peers in its network. The wireless device then sends wireless device identifiers for the nearby wireless peers in a location request to the computing cloud, which estimates a location for the wireless device using the previously recorded location information for the nearby wireless peers. The computing cloud returns the estimated location to the requesting wireless device.

Abstract: Techniques are described for obtaining high-resolution physical locations for a wireless device by leveraging the high-resolution physical location capabilities of wireless peers of the wireless device to provide a peer-to-peer location service and facilitate location targeting. Wireless devices provide location updates to a computing cloud, which stores records associating wireless device identifiers with received location information for the corresponding wireless devices. A wireless device issues a peer discovery request to dynamically identify nearby wireless peers in its network. The wireless device then sends wireless device identifiers for the nearby wireless peers in a location request to the computing cloud, which estimates a location for the wireless device using the previously recorded location information for the nearby wireless peers. The computing cloud returns the estimated location to the requesting wireless device.

Abstract: Techniques are described for obtaining high-resolution physical locations for a wireless device by leveraging the high-resolution physical location capabilities of wireless peers of the wireless device to provide a peer-to-peer location service and facilitate location targeting. Wireless devices provide location updates to a computing cloud, which stores records associating wireless device identifiers with received location information for the corresponding wireless devices. A wireless device issues a peer discovery request to dynamically identify nearby wireless peers in its network. The wireless device then sends wireless device identifiers for the nearby wireless peers in a location request to the computing cloud, which estimates a location for the wireless device using the previously recorded location information for the nearby wireless peers. The computing cloud returns the estimated location to the requesting wireless device.