Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: A system, method, apparatus, and processor readable non-transitive storage media are described for matching items in large datasets based on non-invasive fingerprints of users so that collected metric data for advertisements (media) and behavioral data may be reconciled and analyzed. Since user fingerprints may not generate a unique one to one correspondence or mapping under certain constraints, the various embodiments employ a sampling method that optimally matches the output of a random sampling of non-invasive fingerprints. The use of non-invasive fingerprints and specialized sampling enables the various embodiments to provide advanced analytics for advertising content and metric data in targeted behavior advertising campaigns. To compare impressions with user profile data, the various embodiments employ in part the time stamp dimension of user profiles to generate temporally unique persistent non-invasive fingerprints.

Abstract: A system, method, apparatus and processor readable media are described for real-time prediction of an advertising audience volume through analysis of historical audience data, and tuning of the predicted audience volume. Embodiments enable a user to specify a query for audience volume prediction. Such a query may be a Boolean combination of various audience categories. A time range may be determined that indicates the amount of historical data that is to be analyzed to make the audience volume prediction in real time. Employing the user-specified query, an audience volume prediction may be provided for a future time period, based on an analysis of retrieved historical audience data for the time range. Embodiments may also enable a user to tune the predicted audience volume through modification of the query through one or more iterations.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: A system, method, apparatus, and processor readable storage media are described for real-time computation of statistics between audiences including, but not limited to, correlations, comparative metrics, and/or other statistical analysis for the audiences. Embodiments enable a user to specify a query that defines a specific audience for analysis in real-time. A past time period may be determined that indicates the amount of historical data that is to be analyzed to generate the statistical comparisons and/or correlations. Employing the user-specified query, statistical comparisons and/or correlation may be provided, based on an analysis of retrieved historical audience data for the time period. Embodiments may also enable a user to generate new statistics in real-time through modification of the audience in one or more iterations. Embodiments may also enable a user to filter, sort, and search through audiences by setting desired statistical boundaries for the resulting audience set.

Abstract: A method, system, apparatus, and storage medium for determining that a client device is enabled for persistent identification, and that may operate as a filter for user information based on whether the client is enabled for persistent identification. A first communication is received regarding a request by the client for a web page. At least one application level data structure is then provided for storage by the client, that persistently identifies at least one characteristic of the client. A second communication is then received that includes a representation of the application level data structures provided, and a comparison is made between the data structures provided and the representation received to determine whether the client is enabled for persistent identification. If persistent identification is enabled, the client may then be provided with advertisements or other content related to the client interests or characteristics.

Abstract: Embodiments are directed towards employing a plurality of tag states to control tag suspension based on an asynchronous process that proactively monitors tag performance, response times, and latency. Tags may be in one of multiple states. Tags in a NORMAL state or a FLAGGED state may be enabled for deployment, and tags in a SUSPENSION A state or a SUSPENSION B state may be blocked from deployment. A tag's state may change based on monitoring the tag's latency to determine if latent events occur. Tag latency may be asynchronously monitored independent of web page requests. If latent events occur, then the tag's state may change from NORMAL to FLAGGED, from FLAGGED to SUSPENSION A, or from SUSPENSION B to SUSPENSION_A. If non-latent events occur, then the tag's state may change from SUSPENSION B to FLAGGED or from FLAGGED to NORMAL, while a tag's state may change from SUSPENSION_A to SUSPENSION_B after a predetermined time period.

Abstract: A system, method, apparatus and processor readable media are described for real-time prediction of an advertising audience volume through analysis of historical audience data, and tuning of the predicted audience volume. Embodiments enable a user to specify a query for audience volume prediction. Such a query may be a Boolean combination of various audience categories. A time range may be determined that indicates the amount of historical data that is to be analyzed to make the audience volume prediction in real time. Employing the user-specified query, an audience volume prediction may be provided for a future time period, based on an analysis of retrieved historical audience data for the time range. Embodiments may also enable a user to tune the predicted audience volume through modification of the query through one or more iterations.

Abstract: Embodiments are directed towards employing a plurality of tag states to control tag suspension based on an asynchronous process that proactively monitors tag performance, response times, and latency. Tags may be in one of multiple states. Tags in a NORMAL state or a FLAGGED state may be enabled for deployment, and tags in a SUSPENSION A state or a SUSPENSION B state may be blocked from deployment. A tag's state may change based on monitoring the tag's latency to determine if latent events occur. Tag latency may be asynchronously monitored independent of web page requests. If latent events occur, then the tag's state may change from NORMAL to FLAGGED, from FLAGGED to SUSPENSION A, or from SUSPENSION B to SUSPENSION_A. If non-latent events occur, then the tag's state may change from SUSPENSION B to FLAGGED or from FLAGGED to NORMAL, while a tag's state may change from SUSPENSION_A to SUSPENSION_B after a predetermined time period.

Abstract: A system, method, apparatus and processor readable media are described for real-time prediction of an advertising audience volume through analysis of historical audience data, and tuning of the predicted audience volume. Embodiments enable a user to specify a query for audience volume prediction. Such a query may be a Boolean combination of various audience categories. A time range may be determined that indicates the amount of historical data that is to be analyzed to make the audience volume prediction in real time. Employing the user-specified query, an audience volume prediction may be provided for a future time period, based on an analysis of retrieved historical audience data for the time range. Embodiments may also enable a user to tune the predicted audience volume through modification of the query through one or more iterations.

Abstract: Embodiments are directed towards employing a plurality of tag states to control tag suspension based on an asynchronous process that proactively monitors tag performance, response times, and latency. Tags may be in one of multiple states. Tags in a NORMAL state or a FLAGGED state may be enabled for deployment, and tags in a SUSPENSION A state or a SUSPENSION B state may be blocked from deployment. A tag's state may change based on monitoring the tag's latency to determine if latent events occur. Tag latency may be asynchronously monitored independent of web page requests. If latent events occur, then the tag's state may change from NORMAL to FLAGGED, from FLAGGED to SUSPENSION A, or from SUSPENSION B to SUSPENSION_A. If non-latent events occur, then the tag's state may change from SUSPENSION B to FLAGGED or from FLAGGED to NORMAL, while a tag's state may change from SUSPENSION_A to SUSPENSION_B after a predetermined time period.

Abstract: Embodiments are directed towards employing a plurality of tag states to control tag suspension based on an asynchronous process that proactively monitors tag performance, response times, and latency. Tags may be in one of multiple states. Tags in a NORMAL state or a FLAGGED state may be enabled for deployment, and tags in a SUSPENSION_A state or a SUSPENSION_B state may be blocked from deployment. A tag's state may change based on monitoring the tag's latency to determine if latent events occur. Tag latency may be asynchronously monitored independent of web page requests. If latent events occur, then the tag's state may change from NORMAL to FLAGGED, from FLAGGED to SUSPENSION_A, or from SUSPENSION_B to SUSPENSION_A. If non-latent events occur, then the tag's state may change from SUSPENSION_B to FLAGGED or from FLAGGED to NORMAL, while a tag's state may change from SUSPENSION_A to SUSPENSION_B after a predetermined time period.

Abstract: Embodiments are directed towards employing a plurality of tag states to control tag suspension based on an asynchronous process that proactively monitors tag performance, response times, and latency. Tags may be in one of multiple states. Tags in a NORMAL state or a FLAGGED state may be enabled for deployment, and tags in a SUSPENSION_A state or a SUSPENSION_B state may be blocked from deployment. A tag's state may change based on monitoring the tag's latency to determine if latent events occur. Tag latency may be asynchronously monitored independent of web page requests. If latent events occur, then the tag's state may change from NORMAL to FLAGGED, from FLAGGED to SUSPENSION_A, or from SUSPENSION_B to SUSPENSION_A. If non-latent events occur, then the tag's state may change from SUSPENSION_B to FLAGGED or from FLAGGED to NORMAL, while a tag's state may change from SUSPENSION_A to SUSPENSION_B after a predetermined time period.

Abstract: Embodiments are directed towards employing a plurality of tag states to control tag suspension based on an asynchronous process that proactively monitors tag performance, response times, and latency. Tags may be in one of multiple states. Tags in a NORMAL state or a FLAGGED state may be enabled for deployment, and tags in a SUSPENSION_A state or a SUSPENSION_B state may be blocked from deployment. A tag's state may change based on monitoring the tag's latency to determine if latent events occur. Tag latency may be asynchronously monitored independent of web page requests. If latent events occur, then the tag's state may change from NORMAL to FLAGGED, from FLAGGED to SUSPENSION_A, or from SUSPENSION_B to SUSPENSION_A. If non-latent events occur, then the tag's state may change from SUSPENSION_B to FLAGGED or from FLAGGED to NORMAL, while a tag's state may change from SUSPENSION_A to SUSPENSION_B after a predetermined time period.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.