Abstract: A more efficient over-the-air software push can be facilitated by leveraging a smart scheduling system for vehicles. The smart scheduling system can use location and network capacity data to prioritize over-the-air software pushes for vehicles. For instance, a vehicle, which is only operational during off-peak wireless network hours can receive a software push during the off-peak times because wireless network capacity is not an issue. However, vehicles, which are used primarily during heavy peak wireless network times can receive software in a prioritized manner based on location data, frequency of use, network capacity, etc.

Abstract: A more efficient over-the-air software push can be facilitated by leveraging a smart scheduling system for vehicles. The smart scheduling system can use location and network capacity data to prioritize over-the-air software pushes for vehicles. For instance, a vehicle, which is only operational during off-peak wireless network hours can receive a software push during the off-peak times because wireless network capacity is not an issue. However, vehicles, which are used primarily during heavy peak wireless network times can receive software in a prioritized manner based on location data, frequency of use, network capacity, etc.

Abstract: A more efficient over-the-air software push can be facilitated by leveraging a smart scheduling system for vehicles. The smart scheduling system can use location and network capacity data to prioritize over-the-air software pushes for vehicles. For instance, a vehicle, which is only operational during off-peak wireless network hours can receive a software push during the off-peak times because wireless network capacity is not an issue. However, vehicles, which are used primarily during heavy peak wireless network times can receive software in a prioritized manner based on location data, frequency of use, network capacity, etc.

Abstract: A method and apparatus for assigning a mobile device that is unassigned to a user are disclosed. Mobile phone numbers are used as a surrogate for device. For example, the method implemented via a processor obtains the phone number that is unassigned from a list of phone numbers associated with a billing account number, assigns the phone number that is unassigned to the user, wherein the user is selected from among a plurality of users associated with the billing account number, determines a confidence level for the assigning of the phone number that is unassigned to the user, and performs an analysis to provide a service for the user to whom the phone number is assigned.

Abstract: A more efficient over-the-air software push can be facilitated by leveraging a smart scheduling system for vehicles. The smart scheduling system can use location and network capacity data to prioritize over-the-air software pushes for vehicles. For instance, a vehicle, which is only operational during off-peak wireless network hours can receive a software push during the off-peak times because wireless network capacity is not an issue. However, vehicles, which are used primarily during heavy peak wireless network times can receive software in a prioritized manner based on location data, frequency of use, network capacity, etc.

Abstract: A base location associated with a user of a communication device can be determined. A location identifier component can determine the base location, such as a home location, of the user based on activity data and/or other data, wherein the activity data can be obtained from base stations with which the user's communication device communicates during a defined time period. The location identifier component can designate a defined area as the base location, wherein the defined area can be associated with the base station with which the communication device most frequently communicates during the defined time period or can be an area that is determined based on the activity data and the other data, in accordance with a defined location algorithm. To facilitate maintaining the privacy of users, the activity data can be anonymized and/or the granularity of defining the base location can be adjusted.

Abstract: A method and apparatus for assigning a mobile device that is unassigned to a user are disclosed. Mobile phone numbers are used as a surrogate for device. For example, the method implemented via a processor obtains the phone number that is unassigned from a list of phone numbers associated with a billing account number, assigns the phone number that is unassigned to the user, wherein the user is selected from among a plurality of users associated with the billing account number, determines a confidence level for the assigning of the phone number that is unassigned to the user, and performs an analysis to provide a service for the user to whom the phone number is assigned.

Abstract: Relational data can be visualized in a map representation. A plurality of vertices and edges are determined based on a set of data identifying objects (i.e., vertices) and relationships (i.e., edges). The vertices and edges are embedded in a planar graph and a Voronoi diagram is generated based at least the plurality of vertices within a set of bounding-points located at least a distance away from the vertices of the map. A bounding-box is associated with each of the vertices, and a set of points generated along each bounding-box. The Voronoi diagram is generated based on the vertices and the set of points of each bounding-box. Each resulting Voronoi cell associated with a common vertex is merged to form a plurality of common cells (i.e., map). The size of each bounding-box can correlate to the importance of its associated vertex, and the points along the bounding-box can be randomly perturbed.

Abstract: Systems and methods to provide a recommendation engine that uses implicit feedback observations are provided. A particular method includes receiving accessing data comprising a plurality of implicit feedback observations for a plurality of users. The plurality of users includes a first user that requested a recommendation. Each implicit feedback observation is associated with a particular user and a particular item of a plurality of items. The method includes determining a plurality of preference ratings and a plurality of confidence ratings for each user of the plurality of users for each item based on the plurality of implicit feedback observations. The method includes generating a recommendation list of one or more of the plurality of items for the first user based on the plurality of preference ratings and the plurality of confidence ratings.

Abstract: Relational data can be visualized in a map representation. A plurality of vertices and edges are determined based on a set of data identifying objects (i.e., vertices) and relationships (i.e., edges). The vertices and edges are embedded in a planar graph and a Voronoi diagram is generated based at least the plurality of vertices within a set of bounding-points located at least a distance away from the vertices of the map. A bounding-box is associated with each of the vertices, and a set of points generated along each bounding-box. The Voronoi diagram is generated based on the vertices and the set of points of each bounding-box. Each resulting Voronoi cell associated with a common vertex is merged to form a plurality of common cells (i.e., map). The size of each bounding-box can correlate to the importance of its associated vertex, and the points along the bounding-box can be randomly perturbed.

Abstract: Apparatus and method for summarizing an original large data set with a representative data set. The data elements in both the original data set and the representative data set have the same variables, but there are significantly fewer data elements in the representative data set. Each data element in the representative data set has an associated weight, representing the degree of compression. There are three steps for constructing the representative data set. First, the original data elements are partitioned into separate bins. Second, moments of the data elements partitioned in each bin are calculated. Finally, the representative data set is generated by finding data elements and associated weights having substantially the same moments as the original data set.