DYNAMIC PREFETCHING OF ONTOLOGIES BASED ON ML-BASED EXECUTION PATTERN RECOGNITION

    公开(公告)号:US20200334556A1

    公开(公告)日:2020-10-22

    申请号:US16386371

    申请日:2019-04-17

    Abstract: In one embodiment, a device in a network obtains data indicative of one or more execution sequences of a semantic reasoner. The device trains a machine learning model to predict use of an ontology by the semantic reasoner, based on the data indicative of the one or more execution sequences of the semantic reasoner. The device predicts, using the machine learning model, use of a particular ontology by the semantic reasoner. The device prefetches the particular ontology from another device via the network, prior to the semantic reasoner completing an execution sequence that requires the particular ontology.

    OPTIMIZED DETECTION OF NETWORK DEFECT EXPOSURE IN NETWORK ENVIRONMENT

    公开(公告)号:US20200162343A1

    公开(公告)日:2020-05-21

    申请号:US16368735

    申请日:2019-03-28

    Abstract: Present technology is directed to preferred processing and the verification of diagnostic signatures for a plurality of network defect. The disclosed optimization process is based on expressing each Diagnostic Signature as a minimal sum of product Boolean function of associated network commands, followed by ranking of each command reference in the product terms of the Boolean expression as well as each Boolean product terms of the SOP Boolean expressions, and constructing a decision tree based on the provided rankings to thereby determine a minimum set of commands along with an preferred command dispatch sequence for evaluating a Diagnostic Signature. Further aspects include the translation of both the optimization computation (interpretation of network conditions associated with a network defect) and the computed workflow (dispatch of the command) into a series of declarative rules that can be processed by a machine reasoning engine to thereby automate the optimization process.

    Optimized detection of network defect exposure in network environment

    公开(公告)号:US11546227B2

    公开(公告)日:2023-01-03

    申请号:US16368735

    申请日:2019-03-28

    Abstract: Present technology is directed to preferred processing and the verification of diagnostic signatures for a plurality of network defect. The disclosed optimization process is based on expressing each Diagnostic Signature as a minimal sum of product Boolean function of associated network commands, followed by ranking of each command reference in the product terms of the Boolean expression as well as each Boolean product terms of the SOP Boolean expressions, and constructing a decision tree based on the provided rankings to thereby determine a minimum set of commands along with an preferred command dispatch sequence for evaluating a Diagnostic Signature. Further aspects include the translation of both the optimization computation (interpretation of network conditions associated with a network defect) and the computed workflow (dispatch of the command) into a series of declarative rules that can be processed by a machine reasoning engine to thereby automate the optimization process.

Patent Agency Ranking