Nec Corporation

Nec C Orporation

Jianwu Xu

Hui Joyce Zhang

Haifeng Chen

Guofei Jiang

Biplob Kumar Debnath

Bo Zong

Tanay Kumar Saha

Bin Nie

Kenji Yoshihira

Renqiang Min

Nipun Arora

Ke Zhang

Pallavi Joshi

Pranay Anchuri

Xiao Yu

Kai Zhang

Hancheng Ge

Yuchen Bian

Hossein Hamooni

A computer-implemented method for implementing electronic control unit (ECU) testing optimization includes capturing, within a neural network model, input-output relationships of a plurality of ECUs operatively coupled to a controller area network (CAN) bus within a CAN bus framework, including generating the neural network model by pruning a fully-connected neural network model based on comparisons of maximum values of neuron weights to a threshold, reducing signal connections of a plurality of collected input signals and a plurality of collected output signals based on connection weight importance, ranking importance of the plurality of collected input signals based on the neural network model, generating, based on the ranking, a test case execution sequence for testing a system including the plurality of ECUs to identify flaws in the system, and initiating the test case execution sequence for testing the system.

Electronic control unit testing optimization

A method detects anomalies in a system having sensors for collecting multivariate sensor data including discrete event sequences. The method determines, using a NMT model, pairwise relationships among the sensors based on the data. The method forms sequences of characters into sentences on a per sensor basis, by treating each discrete variable in the sequences as a character in natural language. The method translates, using the NMT, the sentences of source sensors to sentences of target sensors to obtain a translation score that quantifies a pairwise relationship strength therebetween. The method aggregates the pairwise relationships into a multivariate relationship graph having nodes representing sensors and edges denoted by the translation score for a sensor pair connected thereto to represent the pairwise relationship strength therebetween. The method performs a corrective action to correct an anomaly responsive to a detection of the anomaly relating to the sensor pair.

Complex system anomaly detection based on discrete event sequences

Systems and methods for contextual event sequence analysis of system failure that analyzes heterogeneous system event record logs are disclosed. The disclosure relates to analyzing event sequences for system failure in ICT and other computerized systems and determining their causes and propagation chains.

System and method for contextual event sequence analysis

Systems and methods for evaluating another computer system using temporal discrete event analytics are provided. The method includes generating sentences of discrete event sequences for multiple sensors. The method also includes building a sensor relationship network in response to generating the sentences of discrete event sequences. The sensor relationship network is analyzed to determine relationships between the multiple sensors. The method further includes performing fault diagnosis based on the sensor relationship network and the relationships between the multiple sensors.

Temporal discrete event analytics system

Systems and methods for automated software test design and implementation. The system and method being able to establish an initial pool of test cases for testing computer code; apply the initial pool of test cases to the computer code in a testing environment to generate test results; preprocess the test results into a predetermined format; extract metadata from the test results; generate a training sequence; calculate a reward value for the pool of test cases; input the training sequence and reward value into a reinforcement learning agent; utilizing the value output from the reinforcement learning agent to produce a ranking list; prioritizing the initial pool of test cases and one or more new test cases based on the ranking list; and applying the prioritized initial pool of test cases and one or more new test cases to the computer code in a testing environment to generate test results.

Deep Q-network reinforcement learning for testing case selection and prioritization

Methods and systems for system maintenance include identifying patterns in heterogeneous logs. Predictive features are extracted from a set of input logs based on the identified patterns. It is determined that the predictive features indicate a future system failure using a first model. A second model is trained, based on a target sample from the predictive features and based on weights associated with a distance between the target sample and a set of samples from the predictive features, to identify one or more parameters of the second model associated with the future system failure. A system maintenance action is performed in accordance with the identified one or more parameters.

Log-based system maintenance and management

Systems and methods for enabling automated log analysis with controllable resource requirements are provided. A training set for log pattern learning is generated based on heterogeneous logs generated by a computer system. An incremental learning process is implemented to generate a set of log patterns from the training set. The heterogeneous logs are parsed using the set of log patterns. A set of applications is applied to the parsed logs.

Method and system for enabling automated log analysis with controllable resource requirements

A method for implementing automated information technology (IT) system failure recommendation and mitigation includes performing log pattern learning to automatically generate sparse time series for each log pattern for a set of classification logs corresponding to a failure, performing multivariate log time series extraction based on the log pattern learning to generate a failure signature for the set of classification logs, including representing the sparse time series as a run-length encoded sequence for efficient storage and computation, calculating a similarity distance between the failure signature for the set of classification logs and each failure signature from a failure signature model file, determining a failure label for the failure corresponding to a most similar known failure based on the similarity distance, and initiating failure mitigation based on the failure label.

Automated information technology system failure recommendation and mitigation

Systems and methods for computer log retrieval are provided. A system can receive a set of query logs, and transform the set of query logs into a query log multivariate time series. The system accesses log multivariate time series of historical logs, and computes and ranks a similarity distance between the query log multivariate time series and each of the log multivariate time series of the historical logs. The system also retrieves a highest ranked set of historical logs as a most similar set of logs compared to the set of query logs.

Computer log retrieval based on multivariate log time series

Systems and methods are disclosed for parsing logs from arbitrary or unknown systems or applications by capturing heterogeneous logs from the arbitrary or unknown systems or applications; generating one pattern for every unique log message; building a pattern hierarchy tree by grouping patterns based on similarity metrics, and for every group it generates one pattern by combing all constituting patterns of that group; and selecting a set of patterns from the pattern hierarchy tree.

Fast pattern discovery for log analytics

A computer-implemented method for automatically analyzing log contents received via a network and detecting content-level anomalies is presented. The computer-implemented method includes building a statistical model based on contents of a set of training logs and detecting, based on the set of training logs, content-level anomalies for a set of testing logs. The method further includes maintaining an index and metadata, generating attributes for fields, editing model capability to incorporate user domain knowledge, detecting anomalies using field attributes, and improving anomaly quality by using user feedback.

Content-level anomaly detection for heterogeneous logs

A method for diagnosing computer system faults using log retrieval based on joint semantic and syntactic similarities includes receiving a set of query logs, defining joint semantic and syntactic similarities between the set of query logs and respective ones of multiple sets of historical logs based on semantic content and syntactic information obtained for the set of query logs and the multiple sets of historical logs, the multiple sets of historical logs being associated with historical computer system fault diagnoses, retrieving a set of historical logs from the multiple sets of historical logs to obtain a retrieved set of historical logs for computer system fault comparison based on a similarity measure corresponding to each of the multiple sets of historical logs derived from the joint semantic and syntactic similarities, and transmitting the retrieved set of historical logs to one or more computing devices to perform the computer system fault comparison.

Joint semantic and format similarity for large scale log retrieval

A heterogeneous log pattern editing recommendation system and computer-implemented method are provided. The system has a processor configured to identify, from heterogeneous logs, patterns including variable fields and constant fields. The processor is also configured to extract a category feature, a cardinality feature, and a before-after n-gram feature by tokenizing the variable fields in the identified patterns. The processor is additionally configured to generate target similarity scores between target fields to be potentially edited and other fields from among the variable fields in the heterogeneous logs using pattern editing operations based on the extracted category feature, the extracted cardinality feature, and the extracted before-after n-gram feature. The processor is further configured to recommend, to a user, log pattern edits for at least one of the target fields based on the target similarity scores between the target fields in the heterogeneous logs.

Recommender system for heterogeneous log pattern editing operation

Systems and methods for system event searching based on heterogeneous logs are provided. A system can include a processor device operatively coupled to a memory device wherein the processor device is configured to mine a variety of log patterns from various of heterogeneous logs to obtain known-event log patterns and unknown-event log patterns, as well as to build a weighted vector representation of the log patterns. The processor device is also configured to evaluate a similarity between the vector representation of the unknown-event and known-event log patterns, identify a known event that is most similar to an unknown event to troubleshoot system faults based on past actions for similar events to improve an operation of a computer system.

System event search based on heterogeneous logs

A method and system are provided for processing computer log messages for log visualization and log retrieval. The method includes collecting log messages from one or more computer system components, performing a log tokenization process on the log messages to generate tokens, transforming the tokens into log vectors associated with a metric space, performing dimensionality reduction on the metric space to project the metric space into a lower dimensional sub-space, storing similarity distances between respective pairs of the log vectors, and in response to receiving a query associated with a query log message for reducing operational inefficiencies of the one or more computer system components, employing the similarity distances to retrieve one or more similar log messages corresponding to the query log message for reducing the operational inefficiencies of the one or more computer system components.

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Lawrenceville, NJ, United States of America

Jianwu Xu