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 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 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 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 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 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 merging data from feeds from multiple sources of computing device network activity data having heterogenous device identifier namespaces and device identifier to device mappings that change over time, the process including: accessing three or more sources of network activity log data from three or more different sources of network activity data, wherein: for each of the sources of network activity log data, based the respective network activity log data, updating a multi-namespace mapping that maps the external-namespace device identifiers to internal-namespace device identifiers in an internal namespace of a system configured to profile mobile computing devices based on logged network activity data of the mobile computing devices, wherein: the namespace mapping comprises a plurality of external-namespace-specific mappings each mapping a respective type of device identifier in a respective external namespace used in the network activity log data to one or more internal-namespace devic

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 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 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 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, 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: A process of discovering psychographic segments of consumers with unsupervised machine learning. A first set of consumer-behavior is converted into respective consumer-behavior vectors for training an unsupervised machine learning model. The unsupervised machine learning model is trained with the first set of consumer-behavior vectors to determine psychographic segments of consumers. A second set of consumer-behavior records is obtained after determining the psychographic segments of consumers and the second set of consumer-behavior records is converted into respective consumer-behavior vectors. The second set of consumer-behavior vectors is classified as each belonging to at least a respective one of psychographic segments with the trained machine learning model to predict, 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 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 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 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 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 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 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 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.