Abstract: Aspects of the invention include determining whether a first log message written by an application during a first job is a message of interest based on a context of the first log message and a probability that the application writes the message for a same job as the first job. Calculating in response to determining that the first log message is a message of interest and by the processor, a correlation score based on intersecting tokens between the first log message and a second log message. Determining the first log message correlates to the second log message based on comparing the score to a threshold score. Modifying a system log of a mainframe to link the first log message to the second log message based on the correlation.

Abstract: Aspects of the invention include computer systems, computer-implemented methods, and computer program products configured to integrate documentation knowledge with log mining data. A non-limiting example computer-implemented method includes determining a message-message relationship based on log message documentation and building a first subgraph based on the message-message relationship. The method further includes receiving a first message log entry having a message identifier and message field data. A second message log entry is correlated with the first message log entry based on at least one of the message identifier and the message field data. A second subgraph is built that includes the first message log entry and the second message log entry. The method includes building a graph that includes the first subgraph and the second subgraph.

Abstract: Aspects of the invention include computer systems, computer-implemented methods, and computer program products configured to perform message correlation extraction for mainframe operation. A non-limiting example computer-implemented method includes receiving a first message log entry having a message identifier and message field data. The first message log entry is pre-processed to determine the message identifier and to tokenize the message field data. A second message log entry is identified based on at least one of the message identifier and the tokenized message field data. The method further includes determining that the second message log entry is correlated with the first message log entry and providing an output comprising the message correlation between the first message log entry and the second message log entry.

Abstract: According to an aspect a computer-implemented method includes identifying a plurality of metrics and log identifiers that describe similar information as a plurality of documentation-based correlation data. One or more metric pair correlations are identified. One or more log frequency correlations are identified by temporal correlation. A plurality of correlated metric-log pairs is identified. A correlation database is populated with the documentation-based correlation data, the one or more metric pair correlations, the one or more log frequency correlations, and the correlated metric-log pairs to support anomaly detection in one or more monitored computer systems.

Abstract: Techniques include generating a log sequence for new logs that have been received, searching a log sequence database for the log sequence having been generated, and determining that the log sequence is anomalous in response to not finding an identical log sequence in the log sequence database. In response to the log sequence not being found in the log sequence database, the log sequence is compared to a graph of historical log sequences to find a closest sequence path to one or more historical log sequences. An anomaly of the log sequence is diagnosed based on an occurrence at which the log sequence deviates from the closest sequence path of the one or more historical log sequences.

Abstract: Described are techniques for using a dynamic ensemble model. The techniques including training a plurality of machine learning models on training data. The techniques further include identifying a similar subset of the training data that is similar to a dataset for evaluation. The techniques further include assembling a subset of models from the plurality of machine learning models based on performance of the subset of models on the similar subset of the training data. The techniques further include generating an output from the subset of models for the dataset for evaluation.

Abstract: A method and system for relation discovery from operation data includes classifying categories of extracted entities from operation data into three or more classes identified in a knowledge base. A log affiliation of the extracted entities is determined, and relations of the extracted entities are identified according to a log affiliation. The identified relations information of the extracted entities is associated with operation objects of the operation data.

Abstract: A method may be performed using a server. The method may include receiving an identity authentication request associated with a user, generating authentication information based on the identity authentication request, and generating candidate information based on the authentication information such that the authentication information is a subset of the candidate information. The candidate information may be displayed at a randomly selected location. The user may provide input through a graphic input interface to select the authentication information from among the candidate information.

Abstract: Aspects of the invention include constructing a knowledge graph by writing a plurality of data structures to connect correlated log messages in a system log. Detecting an anomalous log message based on the knowledge graph, wherein the anomalous log message is connected to a plurality of candidate root cause error log messages. Determining respective sequences from each of the plurality of candidate root cause error log messages to the anomalous log message. Calculating a deviation score for each respective sequence based on a deviation of an expected sequence for each candidate root cause error log message and the determined sequence. Determining a root cause log error message based on the calculated deviation scores.

Abstract: A stand lock kit and a method for using the same are provided. The stand lock kit of the present invention includes a stand lock and a casing. The stand lock includes a body, a first support arm, and a second support arm. The first and the second support arms are disposed in the body, wherein at least one of them is capable of moving with respect to the body to secure the display of the electronic device between them and to make the relative position of the body fixed. The casing has a casing connecting unit. One end of the casing connecting unit connects to the casing, while a free end of the same is capable of inserting into the first lock hole of the body and being restricted from leaving the first lock hole by the stand lock core. The expansion electronic device is accommodated in the casing.

Abstract: Embodiments of the present disclosure relate to a method for processing alerts. According to an embodiment of the present disclosure, a set of alerts matching a metric template are identified from received alerts during a period of time. A plurality of variable values are acquired from the set of alerts based on the metric template. The plurality of variable values are normalized according to a normalization rule of the metric template. A severity level for the set of alerts is determined based on the normalized variable values. In response to the severity level exceeding a certain threshold, an abstract alert including information related to the set of alerts is generated.

Abstract: A computer-implemented method includes receiving, by a processor, real time operation data related to an application, identifying components of the application based on the operation data, extracting relationships and interdependencies between the components, and generating a graph of the identified components, the relationships and the interdependencies. The method also includes determining one or more dynamic metrics of the identified components, the one or more dynamic metrics indicative of interactions between the components, extracting statistical information describing at least one of performance and resource consumption based on the operation data, incorporating the dynamic metrics into the graph, determining a behavior of at least one component based on a pattern of appearance of the at least one component in the graph, and generating a model of the application based on the identified components and the determined behaviors.

Abstract: A computer-implemented method, system, and non-transitory machine readable medium for a graph-based analysis for an Information Technology (IT) operations includes generating a temporal graph by extracting one or more of operation objects, relations and attributes from operation data of workloads distributed across a plurality of levels of the IT operation within a predetermined time window. Anomalies are detected from the extracted operation data and annotating corresponding objects in the graph. A directional impact between corresponding objects on the temporal graph is determined, and the temporal graph is refined based on the determined directional impact. Accessible paths in the temporal graph indicating error propagation are searched, and potential causes for the detected anomalies in the temporal graph are identified. A list of the potential causes of the anomalies is generated, and a root cause ranked for each of the corresponding objects in the temporal graph.

Abstract: A method and system for relation discovery from operation data includes classifying categories of extracted entities from operation data into three or more classes identified in a knowledge base. A log affiliation of the extracted entities is determined, and relations of the extracted entities are identified according to a log affiliation. The identified relations information of the extracted entities is associated with operation objects of the operation data.

Abstract: A system and method for attribute discovery for operation objects from operation data includes segmenting a name of each of a plurality of operation objects based on one or more special characters used in the name of each operation object. A similarity comparison of the operation objects is performed by extracting common subsequences from substrings in operation data in a same log as a target object, and a string similarity is computed of the extracted common subsequences. Numerical attributes are determined by calculating statistical metrics for fields in the log, and additional information of the operation objects is discovered based on the determined numerical attributes.

Abstract: A method and system for operation objects discovery from operation data includes performing pattern matching of operation data with patterns in a database. Fields in the operation data are identified as having matching patterns with the database as first potential objects. Data profiling is performed on unmatched fields of the operation data to generate data profiles. The data profiles are field classified and second potential objects are generated. The first potential objects and the second potential objects are de-duplicated, and operation objects are generated.

Abstract: A method may be performed using a server. The method may include receiving an identity authentication request associated with a user, generating authentication information based on the identity authentication request, and generating candidate information based on the authentication information such that the authentication information is a subset of the candidate information. The candidate information may be displayed at a randomly selected location. The user may provide input through a graphic input interface to select the authentication information from among the candidate information.

Abstract: When a security authentication request sent by a terminal is received, an identity authentication solution includes acquiring network environment information and user behavior data according to the security authentication request, then determining, according to the network environment information and the user behavior data, whether a current operation is a machine attack, and acquiring a CAPTCHA of a predetermined type according to a predetermined policy and delivering the CAPTCHA to the terminal if the current operation is a machine attack, to perform identity authentication, or determining that security authentication succeeds if the current operation is not a machine attack.

Abstract: An all-in-one electrical receptacle connector includes an audio connector body, an audio connector component, and an electrical connector. The audio connector component is assembled with an audio fixation groove of the audio connector body, and the electrical connector is assembled with a connector fixation groove of the audio connector body. Accordingly, the audio receptacle connector (audio jack) is assembled with receptacle connector(s) with different connection interfaces (e.g., Micro USB, USB type-C, HDMI) to have an all-in-one electrical receptacle connector including the audio receptacle connector and the electrical connector in the electronic product. Therefore, the waterproof function of the housing of the electronic product can be improved effectively.

Abstract: A lock rack contains: a case, a support mounting, and a movable press rod. The case includes a locking orifice. The support mounting includes a first extending fixer and a second extending fixer which are rotatably connected with the case and are meshed with each other, the first extending fixer has a first engagement portion for engaging with a first corner of a lower end of the portable electronic device, and a second extending fixer has a second engagement portion for engaging with a second corner of the lower end of the portable electronic device. The movable press rod is inserted into the case and includes a retainer configured to retain with an upper end of the portable electronic device.