Abstract: Briefly, embodiments disclosed herein relate generally to on-line content sampling, and more particularly to utilization of machine learning techniques to sample on-line content in a search engine environment, for example.

Abstract: Briefly, embodiments disclosed herein relate generally to on-line content sampling, and more particularly to utilization of machine learning techniques to sample on-line content in a search engine environment, for example.

Abstract: Briefly, embodiments of methods and/or systems of computation via array decomposition are disclosed. For one embodiment, as an example, a system may be capable of implementation of an advertising audience overlap analysis dashboard in which for an audience exceeding 100 million users and exceeding 10,000 user groups. Such a system embodiment, for example, may be capable of computing an exact count of user overlap among the user groups in less than two hours.

Abstract: Briefly, embodiments of methods and/or systems of computation via array decomposition are disclosed. For one embodiment, as an example, a system may be capable of implementation of an advertising audience overlap analysis dashboard in which for an audience exceeding 100 million users and exceeding 10,000 user groups. Such a system embodiment, for example, may be capable of computing an exact count of user overlap among the user groups in less than two hours.

Abstract: Techniques are provided for improving the speed and accuracy of analytics on big data using theta sketches, by converting fixed-size sketches to theta sketches, and by performing set operations on sketches. In a technique for performing a set operation, two sketches are analyzed to identify the maximum value of each sketch. The maximum values of the two sketches are compared. Based the comparison, one or more values are removed from the sketch whose maximum value is greater. After the removal, a set operation (e.g., union, intersection, or difference) is performed based on the modified sketch and the unmodified sketch. A result of the set operation is a third sketch, which may be used to estimate a cardinality of the larger data sets that are represented by the two input sketches.

Abstract: A system and method to identify target node graphs from predetermined seed node subsets are described. A one-sided constrained spectral embedding of a graph using a predetermined set of seed nodes within the graph is computed. Next, a threshold boundary is identified to define a target area for a target subgraph within the embedded graph using predetermined network-flow calculations, the target subgraph including the predetermined set of seed nodes. Finally, the target subgraph is identified based on the calculated threshold boundary.

Abstract: A system and method to identify target node graphs from predetermined seed node subsets are described. A one-sided constrained spectral embedding of a graph using a predetermined set of seed nodes within the graph is computed. Next, a threshold boundary is identified to define a target area for a target subgraph within the embedded graph using predetermined network-flow calculations, the target subgraph including the predetermined set of seed nodes. Finally, the target subgraph is identified based on the calculated threshold boundary.

Abstract: In a network, a query originator injects queries of network devices into the network at a query node using query messages. The network transports the query messages to the network devices, or to network nodes at which queries about the network devices can be answered. Query responses from the network devices or network nodes are directed through the network to a collection node or nodes. As an internal network node receives multiple query responses from network devices, the internal network node might aggregate, as needed, the multiple query responses into an aggregated query response that preferably occupies less bandwidth than the aggregated multiple query responses. Where the result desired at the collection node is a computed function of the multiple query responses, the computed function can be performed at each internal network node on the multiple query responses received at that node, thus distributing the computation needed to form a collected response to the query.

Abstract: In a network, a query originator injects queries of network devices into the network at a query node using query messages. The network transports the query messages to the network devices, or to network nodes at which queries about the network devices can be answered. Query responses from the network devices or network nodes are directed through the network to a collection node or nodes. As an internal network node receives multiple query responses from network devices, the internal network node might aggregate, as needed, the multiple query responses into an aggregated query response that preferably occupies less bandwidth than the aggregated multiple query responses. Where the result desired at the collection node is a computed function of the multiple query responses, the computed function can be performed at each internal network node on the multiple query responses received at that node, thus distributing the computation needed to form a collected response to the query.