Abstract: A method of detecting an event anomaly includes receiving one or more data points, in which each data point represents a spatial or temporal event; associating a unique identifier with each of the one or more data points to obtain one or more individualized data points; distributing the one or more individualized data points across a grid, in which the grid includes one or more cells; determining an event likelihood ratio for one or more of the grid cells; identifying one or more event clusters, in which each event cluster includes one or more of the grid cells; and storing in a data repository an event cluster having a significance level above a threshold significance level.

Abstract: A technique is disclosed for ranking venues and for providing a user with one or more of the ranked venues. To do so, a data processing system trains a model during a first, training sequence, by using a training dataset of past check-ins that were made by users visiting venues. The trained model, representing a learned hypothesis, represents venues that are ranked according to their relevance to users who are also represented by the model. During a second, run-time sequence, the system receives real-time geolocation data that represents users and generates a set of candidate venues for a user by determining venues that are nearby the geolocation of the user. The system then ranks the generated candidate venues by applying the learned hypothesis. One or more of the ranked venues can be provided to a user's wireless terminal, for example, where they can be presented as venue recommendations.

Abstract: A technique is disclosed for presenting possible check-in venues to a mobile device user, such as for the purpose of checking in with social networking services such as Foursquare. The disclosed technique operates by training a model during an initial training sequence. The trained model, representing a learned hypothesis, describes venues that are ranked according to their relevance to users who are also described by the model. During a subsequent run-time sequence, the system receives real-time geolocation data that represents users, wherein the data includes user geolocation and the level of accuracy of the geolocation. The system generates a set of candidate venues, ranks the generated candidate venues by applying the learned hypothesis, which can be dependent on the level of accuracy, and presents the user with the ranked venues as possible check-in venues. The user can then select the venue actually being checked into from the presented check-in venues.

Abstract: A description of a user is estimated based on the context of a user's past and present locations. A disclosed data-processing system continually receives data points for each user that represent spatial and/or temporal events. These events represent, for example, presence of a person at a specific geographic location such as a geographic area or point of interest (POI). The data-processing system evaluates the received data points in relation to one or more of the geographic locations, yielding results that are also based on the demographic characteristics of each visited location and the commercial characteristics of each visited location. The data-processing system evaluates the data points also to determine patterns exhibited in each user's activity or inactivity, and patterns exhibited in the distance traveled and the type of travel. The data-processing system bases the user descriptions on the results of these evaluations.

Abstract: A description of a user is estimated based on the context of the user's travel pattern. A disclosed data-processing system continually receives data points originating from the user's cell phone, for example, wherein each data point represents a visit by the user to a particular geolocation, at a particular time. The system then partitions the received data points into groups, wherein the groups represent the different places that the user has visited. The system counts how many data points are in each group. Based on the number of data points in each group, as well as the number of groups, the system then determines the entropy of the received data points that correspond to the particular user, which is also regarded as the entropy of the user's travel pattern. The data-processing system then estimates a user description for the user, based at least in part on the determined user entropy.

Abstract: Systems and computer-implemented methods are provided for comparing, associating and deriving associations between two or more spatial temporal data trails. One or more spatial-temporal data trails comprising one or more places are received at a processor. Each place is identified by a spatial temporal data point. And each spatial-temporal data trail is associated with an individual. The similarity between pairs of places is determined to establish one or more groups of places or one or more groups of individuals. Similarity and/groups can be determined based on demographics associated with the place or individual.

Abstract: A system and method are provided for associating location data from one or more unique sources. The place and time of a unique location enabled device are associated with stored demographic information relating to the particular place and particular time. The place and time of the unique location enabled device are associated with a historical record of past locations and time of locations that the device has been. Based on the association of demographical information and historical information, the unique location enable device is assigned to one or more groups or tribes. The location of all members of the group or tribe can be aggregated and exported for further analysis or display, thereby showing all group or tribe members at a particular time and place.

Abstract: Systems and computer implemented methods are provided for comparing, associating and deriving associations between two or more spatial temporal data trails. One or more spatial-temporal data trails comprising one or more places are received at a processor. Each place is identified by a spatial temporal data point. And each spatial-temporal data trail is associated with an individual. The similarity between pairs of places is determined to establish one or more groups of places or one or more groups of individuals. Similarity and/groups can be determined based on demographics associated with the place or individual.

Abstract: A system and method are provided for associating location data from one or more unique sources. The place and time of a unique location enabled device are associated with stored demographic information relating to the particular place and particular time. The place and time of the unique location enabled device are associated with a historical record of past locations and time of locations that the device has been. Based on the association of demographical information and historical information, the unique location enable device is assigned to one or more groups or tribes. The location of all members of the group or tribe can be aggregated and exported for further analysis or display, thereby showing all group or tribe members at a particular time and place.