Abstract: A system and method for estimating available payload inventory are provided. An advertisement delivery system generates a set of atomic market segment arrays from target market criteria for one or more advertisement campaigns to be served. The set of arrays is incremented corresponding to advertisement requests matching the target market criteria. The atomic market segment is processed to select an advertisement and to predict future capacity and manage inventory.

Abstract: Embodiments relate to generating and using a directed graph as an advertising network taxonomy. Individual delivery locations provided by publishers for presenting advertisements are identified. A directed graph is generated that includes specific advertising opportunities that each correspond with an individual delivery location. The directed graph also includes multiple general advertising opportunities that correspond with aggregations of delivery locations. Multiple aggregations may each include all or part of the same set of delivery locations. Advertisers may then use the directed graph to purchase advertising opportunities, and advertisements may be served to delivery locations based on the advertiser selections.

Abstract: Embodiments relate to generating and using a directed graph as an advertising network taxonomy. Individual delivery locations provided by publishers for presenting advertisements are identified. A directed graph is generated that includes specific advertising opportunities that each correspond with an individual delivery location. The directed graph also includes multiple general advertising opportunities that correspond with aggregations of delivery locations. Multiple aggregations may each include all or part of the same set of delivery locations. Advertisers may then use the directed graph to purchase advertising opportunities, and advertisements may be served to delivery locations based on the advertiser selections.

Abstract: Enhancements to Bayesian prediction models for network location traffic provide increased accuracy in web traffic predictions. The enhancements include implementing user advertising target queries to determine preferred edges of a Bayesian model, employing hierarchical data structures to cleanse training data for a Bayesian model, and/or augmenting existing data with new training data to enhance a previously constructed Bayesian model. Preferred edge enhancements for the Bayesian model utilize target attribute derived preferred edges and/or explicitly specified preferred edges. Training data is cleansed utilizing tag hierarchies that can employ parent-child relationships, ancestor relationships, and/or network location specific parameters. New training data can also be employed to adjust probabilities in a previously constructed Bayesian model. The new training data can be weighted differently than data represented by the previously constructed Bayesian model.

Abstract: Embodiments relate to generating and using a directed graph as an advertising network taxonomy. Individual delivery locations provided by publishers for presenting advertisements are identified. A directed graph is generated that includes specific advertising opportunities that each correspond with an individual delivery location. The directed graph also includes multiple general advertising opportunities that correspond with aggregations of delivery locations. Multiple aggregations may each include all or part of the same set of delivery locations. Advertisers may then use the directed graph to purchase advertising opportunities, and advertisements may be served to delivery locations based on the advertiser selections.

Abstract: Enhancements to Bayesian prediction models for network location traffic provide increased accuracy in web traffic predictions. The enhancements include implementing user advertising target queries to determine preferred edges of a Bayesian model, employing hierarchical data structures to cleanse training data for a Bayesian model, and/or augmenting existing data with new training data to enhance a previously constructed Bayesian model. Preferred edge enhancements for the Bayesian model utilize target attribute derived preferred edges and/or explicitly specified preferred edges. Training data is cleansed utilizing tag hierarchies that can employ parent-child relationships, ancestor relationships, and/or network location specific parameters. New training data can also be employed to adjust probabilities in a previously constructed Bayesian model. The new training data can be weighted differently than data represented by the previously constructed Bayesian model.

Abstract: Random samples without replacement are extracted from a distributed set of items by leveraging techniques for aggregating sampled subsets of the distributed set. This provides a uniform random sample without replacement representative of the distributed set, allowing statistical information to be gleaned from extremely large sets of distributed information. Subset random samples without replacement are extracted from independent subsets of the distributed set of items. The subset random samples are then aggregated to provide a uniform random sample without replacement of a fixed size that is representative of a distributed set of items of unknown size. In one instance, a multivariate hyper-geometric distribution is sampled by breaking up the multivariate hyper-geometric distribution into a set of univariate hyper-geometric distributions.

Abstract: A system and method for estimating available payload inventory are provided. An advertisement delivery system generates a set of atomic market segment arrays from target market criteria for one or more advertisement campaigns to be served. The set of arrays is incremented corresponding to advertisement requests matching the target market criteria. The atomic market segment is processed to select an advertisement and to predict future capacity and manage inventory.