Optimization for Real-Time, Parallel Execution of Models for Extracting High-Value Information from Data Streams

A system for identification of high-value information in data streams, comprising:

• a plurality of processors;
• memory storing one or more programs to be executed by the plurality of processors;
• the one or more programs comprising instructions for:
• receiving a plurality of filter graphs, each filter graph includes a plurality of filter nodes interrelated by a plurality of graph edges, each filter node representing a classification model;
• performing a continuous monitoring process for a data stream that includes a plurality of posts from a plurality of sources, including:
• without user intervention, in response to receiving the data stream with the plurality of posts, distributing the plurality of posts to inputs of the plurality of filter graphs;
• and
• identifying respective ones of the plurality of posts with high-value information with regard to the respective filter graphs, based on parallel execution of the filter nodes included in the respective filter graph, by applying predefined criteria with respect to classification models of corresponding filter nodes to the plurality of posts,
• wherein applying the predefined criteria to a respective post of the plurality of posts includes:
• executing one or more textual filters on text content of the post in accordance with a first of the classification models;
• and
• determining whether to tag the post with an identifier of the first classification model or to tag the post as rejected with the identifier of the first classification model based on whether the post is accepted by the first classification model.