Abstract: Online consumption data may be secured by receiving data associated with first online interactions actually performed during a predetermined time period, generating, via a machine learning model for each of a plurality of different personas, data associated with second online interactions that simulate Internet traffic, selecting a plurality of the received data associated with the first online interactions that matches the generated data associated with the second online interactions of one or more of the personas, replacing the generated data associated with the second online interactions of the one or more personas with the selected data, and outputting the one or more personas with the replaced data.

Abstract: Aggregating viewership data is disclosed. Initially, household viewership data assigned to a tuning event in multiple households is accessed. Then, the household viewership data for the tuning event in the multiple households representing the same episode is aggregated to generate episode viewership data. Next, episode viewership data representing multiple episodes of the same program is aggregated to generate program viewership data. Next, the program viewership data for multiple programs of the same network is aggregated to generate network viewership data. Finally, the network viewership data for multiple commonly-owned networks is aggregated to generate entity viewership data.

Abstract: Techniques for projecting person-level viewership from household-level tuning events are described. Initially, panelist viewing data are accessed and a plurality of state values based on the panelist viewing data are determined. Then, tuning data representing tuning events associated with particular households are accessed. For at least one tuning event represented by the tuning data, household member data is accessed, a portion of the panelist viewing data whose panelist information matches at least a portion of the member data is determined, a total number of watched minutes of the program by an individual member and a number of continuous series of watched states of the program by the individual member is determined, and an output representative of a probability that the particular portion of the program was watched by one or more of the individual members is generated.

Abstract: A reporting server may access a first data set that indicates an association between one or more device identifiers and one or more content identifiers and a second data set that indicates an association between the one or more device identifiers and one or more user identifiers. The reporting server may determine, based on the first data set and the second data set, that at least one user associated with a user identifier of the second data set has accessed content associated with a plurality of the content identifiers of the first data set, and may generate a report that represents, for each content identifier, a number of unique user identifiers associated with the content identifier.

Abstract: Techniques for generating a deterministic outcome for content viewership based on a probability model are described. Information that identifies a probability that a particular portion of a program was viewed by an individual member of a household may be accessed. A probability distribution may be generated that represents, for each of a plurality of state spaces, a probability that the particular portion of the program was viewed by the plurality of individual members of the household. Based on a simulation of the generated probability distribution, a plurality of possible viewership scenarios may be determined. One of the plurality of viewership scenarios may be selected, and a report may be generated that identifies, for the selected viewership scenario, each of the individual members of the household and an indication of whether the particular portion of the program was viewed by each of the individual members of the household.

Abstract: Techniques for projecting person-level viewership from household-level tuning events are described. Initially, panelist viewing data are accessed and a plurality of state values based on the panelist viewing data are determined. Then, tuning data representing tuning events associated with particular households are accessed. For at least one tuning event represented by the tuning data, household member data is accessed, a portion of the panelist viewing data whose panelist information matches at least a portion of the member data is determined, a total number of watched minutes of the program by an individual member and a number of continuous series of watched states of the program by the individual member is determined, and an output representative of a probability that the particular portion of the program was watched by one or more of the individual members is generated.

Abstract: Aggregating viewership data is disclosed. Initially, household viewership data assigned to a tuning event in multiple households is accessed. Then, the household viewership data for the tuning event in the multiple households representing the same episode is aggregated to generate episode viewership data. Next, episode viewership data representing multiple episodes of the same program is aggregated to generate program viewership data. Next, the program viewership data for multiple programs of the same network is aggregated to generate network viewership data. Finally, the network viewership data for multiple commonly-owned networks is aggregated to generate entity viewership data.

Abstract: Tuning data representing a television viewing event associated with a particular household is accessed. Household member data representing information on individual members of the particular household is accessed. Viewing profile data representing information on individual members of other households regarding viewership by the individual members of the other households is accessed. Fractional viewership values for the individual members of the particular household are determined based on the tuning data, the household member data, and the viewing profile data. Household viewership data is determined based on the fractional viewership values.

Abstract: Tuning data representing a television viewing event associated with a particular household is accessed. Household member data representing information on individual members of the particular household is accessed. Viewing profile data representing information on individual members of other households regarding viewership by the individual members of the other households is accessed. Fractional viewership values for the individual members of the particular household are determined based on the tuning data, the household member data, and the viewing profile data. Household viewership data is determined based on the fractional viewership values.

Abstract: Tuning data representing a television viewing event associated with a particular household is accessed. Household member data representing information on individual members of the particular household is accessed. Viewing profile data representing information on individual members of other households regarding viewership by the individual members of the other households is accessed. Fractional viewership values for the individual members of the particular household are determined based on the tuning data, the household member data, and the viewing profile data. Household viewership data is determined based on the fractional viewership values.

Abstract: A first set of usage data for a first set of resources on a network and a second set of usage data for a second set of resources on a network are accessed. The first set of resources were accessed by a first group of client systems and the first set of usage data is determined based on information received from the first group of client systems sent as a result of beacon instructions included with the first set of resources. The second set of usage data is determined based on information received from monitoring applications installed on a second group of client systems that accessed the second set of resources. Users of the second group of client systems are a sample of a larger group of users that use resources on the network. Initial usage measurement data for a third set of resources on the network is determined based on the first set of usage data. The third set includes one or more common resources that are included in the first set of resources and the second set of resources.

Abstract: Projection weights may be configured to project usage of a first set of resources by users of a first group of clients systems to usage of the first set of resources by users in a larger group of users. Users of the first group of client systems may be a sample of a larger group of users that access resources on the network using client systems. The projection weights may be determined based on a first set of usage data and a second set of usage data. The first set of usage data may be determined based on information received from monitoring applications installed on a first group of client systems. The second set of usage data may be determined based on information received from a second group of client systems sent as a result of beacon instructions included with a second set of resources accessed by the second group of client systems.

Abstract: A first set of usage data for a first set of resources on a network and a second set of usage data for a second set of resources on a network are accessed. The first set of resources were accessed by a first group of client systems and the first set of usage data is determined based on information received from the first group of client systems sent as a result of beacon instructions included with the first set of resources. The second set of usage data is determined based on information received from monitoring applications installed on a second group of client systems that accessed the second set of resources. Users of the second group of client systems are a sample of a larger group of users that use resources on the network. Initial usage measurement data for a third set of resources on the network is determined based on the first set of usage data. The third set includes one or more common resources that are included in the first set of resources and the second set of resources.

Abstract: Projection weights may be configured to project usage of a first set of resources by users of a first group of clients systems to usage of the first set of resources by users in a larger group of users. Users of the first group of client systems may be a sample of a larger group of users that access resources on the network using client systems. The projection weights may be determined based on a first set of usage data and a second set of usage data. The first set of usage data may be determined based on information received from monitoring applications installed on a first group of client systems. The second set of usage data may be determined based on information received from a second group of client systems sent as a result of beacon instructions included with a second set of resources accessed by the second group of client systems.