Abstract: A computer system identifies high-value information in data streams. The computer system receives a filter graph definition. The filter graph definition includes a plurality of filter nodes, each filter node including one or more filters that accept or reject packets. Each respective filter is categorized by a number of operations, and the one or more filters are arranged in a general graph. The computer system performs one or more optimization operations, including: determining if a closed circuit exists within the graph, and when the closed circuit exists within the graph, removing the closed circuit; reordering the filters based at least in part on the number of operations; and parallelizing the general graph such that the one or more filters are configured to be executed on one or more processors.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electric posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A computer system identifies high-value information in data streams. The computer system receives a filter graph definition. The filter graph definition includes a plurality of filter nodes, each filter node including one or more filters that accept or reject packets. Each respective filter is categorized by a number of operations, and the one or more filters are arranged in a general graph. The computer system performs one or more optimization operations, including: determining if a closed circuit exists within the graph, and when the closed circuit exists within the graph, removing the closed circuit; reordering the filters based at least in part on the number of operations; and parallelizing the general graph such that the one or more filters are configured to be executed on one or more processors.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electric posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A computer system identifies high-value information in data streams. The computer system receives a filter graph definition. The filter graph definition includes a plurality of filter nodes, each filter node including one or more filters that accept or reject packets. Each respective filter is categorized by a number of operations, and the one or more filters are arranged in a general graph. The computer system performs one or more optimization operations, including: determining if a closed circuit exists within the graph, and when the closed circuit exists within the graph, removing the closed circuit; reordering the filters based at least in part on the number of operations; and parallelizing the general graph such that the one or more filters are configured to be executed on one or more processors.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A computer system identifies high-value information in data streams. The computer system receives a filter graph definition. The filter graph definition includes a plurality of filter nodes, each filter node including one or more filters that accept or reject packets. Each respective filter is categorized by a number of operations, and the one or more filters are arranged in a general graph. The computer system performs one or more optimization operations, including: determining if a closed circuit exists within the graph, and when the closed circuit exists within the graph, removing the closed circuit; reordering the filters based at least in part on the number of operations; and parallelizing the general graph such that the one or more filters are configured to be executed on one or more processors.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A computer system identifies high-value information in data streams. The computer system receives a filter graph definition. The filter graph definition includes a plurality of filter nodes, each filter node including one or more filters that accept or reject packets. Each respective filter is categorized by a number of operations, and the one or more filters are arranged in a general graph. The computer system performs one or more optimization operations, including: determining if a closed circuit exists within the graph, and when the closed circuit exists within the graph, removing the closed circuit; reordering the filters based at least in part on the number of operations; and parallelizing the general graph such that the one or more filters are configured to be executed on one or more processors.

Abstract: A computer system identifies high-value information in data streams. The computer system receives a filter graph definition. The filter graph definition includes a plurality of filter nodes, each filter node including one or more filters that accept or reject packets. Each respective filter is categorized by a number of operations, and the one or more filters are arranged in a general graph. The computer system performs one or more optimization operations, including: determining if a closed circuit exists within the graph, and when the closed circuit exists within the graph, removing the closed circuit; reordering the filters based at least in part on the number of operations; and parallelizing the general graph such that the one or more filters are configured to be executed on one or more processors.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A computer system identifies high-value information in data streams. The computer system receives a filter graph definition. The filter graph definition includes a plurality of filter nodes, each filter node including one or more filters that accept or reject packets. Each respective filter is categorized by a number of operations, and the one or more filters are arranged in a general graph. The computer system performs one or more optimization operations, including: determining if a closed circuit exists within the graph, and when the closed circuit exists within the graph, removing the closed circuit; reordering the filters based at least in part on the number of operations; and parallelizing the general graph such that the one or more filters are configured to be executed on one or more processors.

Abstract: A computer system identifies high-value information in data streams. The computer system receives a filter graph definition. The filter graph definition includes a plurality of filter nodes, each filler node including one or more filters that accept or reject packets. Each respective filter is categorized by a number of operations, and the one or more filters are arranged in a general graph. The computer system performs one or more optimization operations, including: determining if a closed circuit exists within the graph, and when the closed circuit exists within the graph, removing the closed circuit; reordering the filters based at least in part on the number of operations; and parallelizing the general graph such that the one or more filters are configured to be executed on one or more processors.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method for controlling distribution of network communications (messages). An incoming message either carries priority information, or is assigned priority information based on a shared characteristic with other messages. The priority information is used to determine how and/or when to deliver the message, e.g. by delaying the message for a fixed time. Preferences for receipt of messages by priority level may be communication to upstream hosts along a network path. Accordingly, an intermediary host may reject and/or delay messages that the intended recipient does not wish to receive. This pushes the burden of low-priority messages back to the sender, thereby reducing or eliminating burdens on network/system resources of the recipient and/or intermediaries between the recipient and the sender. Accordingly, it can “squelch” spam messages at or close to their source. Trusted senders complying with prescribed practices may include priority information allowing for delivery of their messages with higher priority.

Abstract: A method for controlling distribution of network communications (messages). An incoming message either carries priority information, or is assigned priority information based on a shared characteristic with other messages. The priority information is used to determine how and/or when to deliver the message, e.g. by delaying the message for a fixed time. Preferences for receipt of messages by priority level may be communication to upstream hosts along a network path. Accordingly, an intermediary host may reject and/or delay messages that the intended recipient does not wish to receive. This pushes the burden of low-priority messages back to the sender, thereby reducing or eliminating burdens on network/system resources of the recipient and/or intermediaries between the recipient and the sender. Accordingly, it can “squelch” spam messages at or close to their source. Trusted senders complying with prescribed practices may include priority information allowing for delivery of their messages with higher priority.

Abstract: A method for controlling distribution of network communications (messages). An incoming message either carries priority information, or is assigned priority information based on a shared characteristic with other messages. The priority information is used to determine how and/or when to deliver the message, e.g. by delaying the message for a fixed time. Preferences for receipt of messages by priority level may be communication to upstream hosts along a network path. Accordingly, an intermediary host may reject and/or delay messages that the intended recipient does not wish to receive. This pushes the burden of low-priority messages back to the sender, thereby reducing or eliminating burdens on network/system resources of the recipient and/or intermediaries between the recipient and the sender. Accordingly, it can “squelch” spam messages at or close to their source. Trusted senders complying with prescribed practices may include priority information allowing for delivery of their messages with higher priority.