Abstract: Architecture that enables geofence combinations and compositions where multiple correlated geofences are generated for an entity such as a point of interest. The geofences can have varying radii relative to a specific entity and represent distinct areas or aspects of the entity. The geofences can relate to correspondingly different categories to which the entity can belong. The geofences can be of differing shapes than circular, such as polygons (e.g., rectangles, squares, etc.). Moreover, these differently shaped geofences can be applied to a single entity. Each geofence of a geofence set associated with an entity can be assigned to represent different parts of an entity such as a part a shopping mall. Geofence composition is obtained by combining multiple primitive geofences to compose more complex geofence(s) for an entity and for embedding the relationship of the primitive geofences into such compositions.

Abstract: Architecture that enables geofence combinations and compositions where multiple correlated geofences are generated for an entity such as a point of interest. The geofences can have varying radii relative to a specific entity and represent distinct areas or aspects of the entity. The geofences can relate to correspondingly different categories to which the entity can belong. The geofences can be of differing shapes than circular, such as polygons (e.g., rectangles, squares, etc.). Moreover, these differently shaped geofences can be applied to a single entity. Each geofence of a geofence set associated with an entity can be assigned to represent different parts of an entity such as a part a shopping mall. Geofence composition is obtained by combining multiple primitive geofences to compose more complex geofence(s) for an entity and for embedding the relationship of the primitive geofences into such compositions.

Abstract: Architecture that enables geofence combinations and compositions where multiple correlated geofences are generated for an entity such as a point of interest. The geofences can have varying radii relative to a specific entity and represent distinct areas or aspects of the entity. The geofences can relate to correspondingly different categories to which the entity can belong. The geofences can be of differing shapes than circular, such as polygons (e.g., rectangles, squares, etc.). Moreover, these differently shaped geofences can be applied to a single entity. Each geofence of a geofence set associated with an entity can be assigned to represent different parts of an entity such as a part a shopping mall. Geofence composition is obtained by combining multiple primitive geofences to compose more complex geofence(s) for an entity and for embedding the relationship of the primitive geofences into such compositions.

Abstract: Architecture that enables the capability to more effectively define and resize geofences to provide improved geofence utility based on rich context and crowd-sourced data. The architecture enables the intelligent placement of geofences based on rich context that includes both user context and ambient context such as the (predicted or implicitly/explicitly defined) user's travel path, mode of transport, the type of the entity to be visited by the user and geofenced, and the user incentive for visiting the entity to be geofenced. The ambient context includes non-user specific information such as external conditions that may limit or thwart user mobility such as traffic and weather conditions. The rich context and crowd-sourced data assist in improving the spatiotemporal accuracy of suggested/constructed geofences thereby creating a “shaped” geofence that is sufficiently defined to approximate the shape of the entity being geofenced with some degree of accuracy.

Abstract: Architecture that obtains and utilizes collections of geographically-tagged data to discover optimal vantage points for viewsheds of entities of interest such as physical entities and conceptual entities such as landmarks, sunset, skyline, etc. The disclosed architecture discloses the utilization of at least geo-tagged image data to discover relationships between a combination of concrete entities and/or abstract concepts, and techniques for surfacing such relationships to users. The data can be crowd-sourced geo-tagged image data that are mined from social content and which can be observed or experienced from a certain location/area.

Abstract: Architecture that enables geofence combinations and compositions where multiple correlated geofences are generated for an entity such as a point of interest. The geofences can have varying radii relative to a specific entity and represent distinct areas or aspects of the entity. The geofences can relate to correspondingly different categories to which the entity can belong. The geofences can be of differing shapes than circular, such as polygons (e.g., rectangles, squares, etc.). Moreover, these differently shaped geofences can be applied to a single entity. Each geofence of a geofence set associated with an entity can be assigned to represent different parts of an entity such as a part a shopping mall. Geofence composition is obtained by combining multiple primitive geofences to compose more complex geofence(s) for an entity and for embedding the relationship of the primitive geofences into such compositions.

Abstract: Architecture that enables the capability to more effectively define and resize geofences to provide improved geofence utility based on rich context and crowd-sourced data. The architecture enables the intelligent placement of geofences based on rich context that includes both user context and ambient context such as the (predicted or implicitly/explicitly defined) user's travel path, mode of transport, the type of the entity to be visited by the user and geofenced, and the user incentive for visiting the entity to be geofenced. The ambient context includes non-user specific information such as external conditions that may limit or thwart user mobility such as traffic and weather conditions. The rich context and crowd-sourced data assist in improving the spatiotemporal accuracy of suggested/constructed geofences thereby creating a “shaped” geofence that is sufficiently defined to approximate the shape of the entity being geofenced with some degree of accuracy.

Abstract: Architecture that employs crowd-sourced parking-related information to compute the probability of finding parking spots at specific road segments, parking lots, and/or in larger geographic areas. The crowd-sourced parking-related information can be obtained from geolocation (geographical location) traces. This approach utilizes a method of mining location traces to compute the probability of finding parking spots at specific road segments, parking lots, and/or in larger geographic areas. The location traces can be mined to classify parking areas as public, private, and semi-private (e.g., only for company employees in certain area that also include public parking areas). The location traces can be mined to infer the times and dates (e.g., hours of the day and the days of the week) during which a vehicle is allowed to park at a given location.

Abstract: Architecture that employs crowd-sourced parking-related information to compute the probability of finding parking spots at specific road segments, parking lots, and/or in larger geographic areas. The crowd-sourced parking-related information can be obtained from geolocation (geographical location) traces. This approach utilizes a method of mining location traces to compute the probability of finding parking spots at specific road segments, parking lots, and/or in larger geographic areas. The location traces can be mined to classify parking areas as public, private, and semi-private (e.g., only for company employees in certain area that also include public parking areas). The location traces can be mined to infer the times and dates (e.g., hours of the day and the days of the week) during which a vehicle is allowed to park at a given location.

Abstract: A location data structure represents a location in a number of different ways, and may include a map view scale appropriate for rending the map. A map view data structure contains sufficient information to derive a number of independent descriptions of a map view. A route data structure may be used at any point in the route calculation and rendering process, and includes a locations field that includes the two-end points of the route, a calculated route data field representing a calculated route between the two end-points, an options field that specifies the options used or to be used to calculate the route, a driving directions field that represents driving directions for the route, and an identity data field that specifies whether the route data structure is a request to calculate a route, a response to a route calculation request, or a rendering request.

Abstract: A location data structure represents a location in a number of different ways, and may include a map view scale appropriate for rending the map. A map view data structure contains sufficient information to derive a number of independent descriptions of a map view. A route data structure may be used at any point in the route calculation and rendering process, and includes a locations field that includes the two-end points of the route, a calculated route data field representing a calculated route between the two end-points, an options field that specifies the options used or to be used to calculate the route, a driving directions field that represents driving directions for the route, and an identity data field that specifies whether the route data structure is a request to calculate a route, a response to a route calculation request, or a rendering request.