Abstract: Provided is a process including: obtaining network traffic logs; matching a set of a plurality of the user computing devices; obtaining an indicator of content to be measured for effectiveness in driving place visits; selecting from the network traffic logs records of network exchanges in which the indicator is present; obtaining a device identifier from each of the selected records; matching the obtained device identifiers to respective matching sets including the respective obtained identifier; obtaining respective mobile device identifiers from the respective matching sets; selecting from the network traffic logs records indicating communications from mobile computing devices identified by the obtained mobile device identifiers; obtaining geolocations from the records indicating communications from the mobile computing devices identified; determining that a subset of the records have geolocations that correspond to at least one of a set of geographic areas; and determining an effectiveness of the content i

Abstract: Provided is a process, including: obtaining geolocations histories of computing devices; assigning different subsets of the location histories to different computing devices in a compute cluster; querying a geographic information system (GIS) with geolocations in the geolocations histories to obtain identifiers of chain retail establishments; determining visit graphs for the individuals; and determining, for a given retail chain and a given individual, a score indicative of an affinity of the given individual to the given retail chain based on the visit graphs for more than 100 individuals including the given individual.

Abstract: Provided is a process of discovering psychographic segments of consumers with unsupervised machine learning, the process including: obtaining a first set of consumer-behavior records; converting the first set of consumer-behavior records into respective consumer-behavior vectors; determining psychographic segments of consumers by training an unsupervised machine learning model with the first set of consumer-behavior vectors; obtaining a second set of consumer-behavior records after determining the psychographic segments of consumers; converting the second set of consumer-behavior records into respective consumer-behavior vectors; classifying the second set of consumer-behavior vectors as each belonging to at least a respective one of psychographic segments with the trained machine learning model; and predicting based on the classification a likelihood of the respective consumer engaging in behavior associated with a corresponding one of the psychographic segments.

Abstract: Provided is a process including: obtaining activity profiles; for each activity profile, sorting the activity records in order of the timestamps; obtaining a query having a rule specifying criteria to select a subset of the individuals; and for each sorted activity profile: iterating through the sorted activity records in sorted order and at each iteration: determining whether the attribute of the geolocation of the respective activity record matches the activity of the activity pattern and, in response to determining a match: determining the activity pattern count; determining whether the activity pattern count satisfies the first condition and, in response to determining that the first condition is satisfied: initializing the activity pattern count; determining the quantifier count; and determining whether the quantifier count satisfies the second condition and, in response designating the individual corresponding to the respective sorted activity profile as responsive to the query.

Abstract: Provided is a process including: obtaining device identifiers of a population of user computing devices; obtaining groups of the users computing devices obtaining one or more places of interest; assigning user computing devices to either a treatment collection xor a control collection based on hash values of the device identifiers; directing application of the treatment according to the assignment; obtaining geolocations visited by the user computing devices; assigning the geolocations to either the treatment collection xor the control collection based on hash values of device identifiers associated with the geolocations; assigning the geolocations to one or more of the groups based on the device identifiers associated with the geolocations; and for each group, determining a respective amount of visits to at least some of the one or more places of interest attributable to the treatment based on the geolocation assignments.

Abstract: Provided is a process of profiling a user of a mobile computing device, the process including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining, with a computer, a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.

Abstract: Provided is a process having steps including obtaining a specification of a data analysis to be performed in parallel on a computing cluster; parsing the specification of the data analysis; determining which data is implicated in each portion of the data analysis to be assigned to a plurality of computing nodes of the computing cluster; determining that a portion of the implicated data is not already present in memory of at least some of the plurality of computing nodes of the computing cluster; distributing the portion of the implicated data according to an index that positions related values of the data on the same computing nodes of the computing cluster; determining which computing nodes of the computing cluster have data relevant to which rules in the data analysis and send relevant rules to the corresponding computing nodes; executing the rules on the computing nodes; and aggregating results of executing the rules.

Abstract: Provided is a process of profiling a user of a mobile computing device, the process including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining, with a computer, a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.

Abstract: Provided is a process of inferring a user's reason for movement between geolocations sensed by a mobile device of the user, the process including: obtaining a history of time-stamped geolocations of a user; selecting a plurality of geographic areas based on each of the selected geographic areas including at least one of the time-stamped geolocations; obtaining a probabilistic model specifying parameters comprising: a plurality of candidate user events, each candidate user event being an underlying potential reason why the user moved between geographic locations; probabilities of the user transitioning between each pair of the candidate user events; and probabilities of obtaining a geolocation reported by the computing devices associated with the user in each of the plurality of geographic areas following occurrence of each of the candidate user events; inferring, with one or more processors, one of the candidate user events.

Abstract: Provided is a process of learning an audience member function, the process including: obtaining a training set of geographic data describing geolocation histories of a plurality of mobile devices, wherein members of the training set are classified according to whether the respective member of the training set is a member of an audience; retrieving attributes of geolocations in the geolocation histories from a geographic information system; learning feature functions of an audience member function based on the training set, wherein at least some of the feature functions are a function of the retrieved attributes of geolocation, wherein the feature functions are learned, at least in part, by calculating a plurality of impurity measures for candidate feature functions and selecting one of the candidate feature functions based on the relative values of the impurity measures; and storing the feature functions of the audience member function in an audience repository.

Abstract: Provided is a process of ascertaining the accuracy of geolocations in a collection of location histories, the process including: obtaining a collection of location histories describing user geolocations, each location history including: a location-history identifier distinguishing the respective location history from other location histories among the collection of location histories, and time-stamped geolocation coordinates specifying geographic locations associated with a respective mobile computing device, the collection of location histories describing geolocations of a plurality of mobile computing; analyzing the collection of location histories by, at least in part, calculating one or more quality attributes of the collection of location histories indicative of differences between the collection of location histories and other collections of location histories known to be of adequate quality; calculating one or more quality scores based on the one or more quality attributes; and storing the one or more

Abstract: Provided is a process of obtaining a plurality of location data sets from different providers of user geolocation history, each location data set including a plurality of user-activity records, each user-activity records being associated with a user identifier and including geolocations of the corresponding user and times that the corresponding user was at the geolocations, the different providers having different user identifiers for a given corresponding user; matching, by one or more processors, the user identifiers between the location data sets based on geolocations of the corresponding user and times that the corresponding user was at the geolocations; and storing the matched user identifiers in association with one another in corresponding user profiles.

Abstract: Provided is a process of augmenting a geographic information system by projecting lower-geographic-resolution data on to higher-geographic-resolution areas, the process including: obtaining aggregate values each describing an attribute of different coarser geographic areas; obtaining a geographic-information system (GIS) having records each describing attributes of a finer-grained geographic area; identifying, for each of the coarser geographic areas, corresponding finer-grained geographic areas of the GIS; projecting, for each of the coarser geographic areas, the aggregate value of the respective coarser geographic area unevenly onto the corresponding finer-grained geographic areas based on respective attribute values of the corresponding finer-grained geographic areas; and storing in memory of the GIS.

Abstract: Provided is a computer-implemented method that includes obtaining a user location dataset indicative of a location visited by a user, the user location dataset being based at least in part on a location signal received from a mobile computing device associated with the user, determining that the location visited corresponds to a geographic location of a place of interest, determining, using a computer, a visit count corresponding to the location dataset, adjusting a visit count for the place of interest to include the visit count corresponding to the location dataset, and storing the adjusted visit count for the place of interest in a visitation rate datastore.

Abstract: Provided is a computer-implemented method that includes obtaining a user location dataset indicative of a location visited by a user, the user location dataset being based at least in part on a location signal received from a mobile computing device associated with the user, determining that the location visited corresponds to a geographic location of a place of interest, determining, using a computer, a visit count corresponding to the location dataset, adjusting a visit count for the place of interest to include the visit count corresponding to the location dataset, and storing the adjusted visit count for the place of interest in a visitation rate datastore.

Abstract: Provided is a process of profiling a user of a mobile computing device, the process including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining, with a computer, a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.

Abstract: Provided is a process of profiling a user of a mobile computing device, the process including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining, with a computer, a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.

Abstract: A technique is disclosed that changes one or more properties of a user interface at a telecommunications terminal based on an incoming call. When a first caller calls the telephone number of the terminal directly and the terminal answers the call, the terminal presents the direct call to the user through a first user interface. When a second caller calls a telephone number that is associated with a data-processing system and the call is then redirected to the terminal, the terminal presents the redirected call to the user through a second user interface. The telecommunications terminal can be a cell phone that belongs to a user, and the data-processing system can be a private branch exchange that serves an office enterprise network that the user belongs to, wherein the private branch exchange extends the call to the user's cell phone when the user is out of the office.

Abstract: An apparatus and method are disclosed that enables detecting the spoofing of a telephone number. To validate the identity of a calling terminal, some techniques in the prior art rely on exchanging data that only the legitimate calling system and the called system know about. In contrast, the illustrative embodiment of the present invention validates the identity of the calling terminal by assessing characteristics other than the calling terminal's telephone number, such as the telephone type and the signaling protocol. By using characteristics other than telephone number to validate the identity, the data-processing system of the illustrative embodiment makes it more difficult to spoof a legitimate telephone's number.

Abstract: A method and apparatus are disclosed that enable a user who is on a call using a call appearance at a first terminal to continue the call on a call appearance at a second terminal, wherein the second terminal has been excluded from the call. In the illustrative embodiment of the present invention, a private branch exchange that handles both telecommunications terminals receives a first signal that indicates that the first terminal has disconnected from the call. The private branch exchange then receives a second signal that indicates that the second terminal has gone off-hook or otherwise indicates a request to be added to the call. If the private branch exchange has received the second signal within a predetermined time interval after having received the first signal, then it adds the second terminal to the call.