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: Techniques include collecting current logs from distributed sources, selecting a group of the current logs that are from a related source of the distributed sources, and generating a feature vector using the group of the current logs. A current status model is created for the feature vector using the group of the current logs. One or more anomalies are determined in the group of the current logs based on a difference between the current status model and a reference status model, the reference status model being based on history logs.

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: 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: 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: Embodiments facilitating performance anomaly detection are described. A computer-implemented method comprises: detecting, by a device operatively coupled to one or more processing units, based on monitoring data of a plurality of performance metrics of a monitored device, at least one trend within the monitoring data of the respective performance metrics; removing, by the device, the at least one trend from the monitoring data of the respective performance metrics to generate modified data of the respective performance metrics; and detecting, by the device, a performance anomaly based on the modified data of the respective performance metrics and a behavior clustering model comprising at least one steady state.

Abstract: Systems and methods for ticket classification and response include labeling tickets with a ticket classifier that assigns a ticket label and an associated confidence score to each ticket. Tickets are clustered according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined. Templates and the respective ticket clusters are clustered according to semantic similarity to form one or more ticket super-clusters. Tickets that have below-threshold confidence scores are labeled according to the one or more ticket super-clusters. The tickets are automatically responded to.

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: Techniques for Bayesian-based event grouping are provided. One technique includes determining a group of alarm events from received alarm events; in response to the group of alarm events matching a group of historical alarm events, determining a first correlation, wherein the group of historical alarm events comprises correlated events associated with a same entity; and determining a root cause of the group of alarm events based on the first correlation.

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 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 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 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 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, system, and computer program product, include extracting information related to one or more processes of one or more applications running on a virtual machine from a memory of the virtual machine, building at least one first application signature based on the extracted information, and identifying the one or more applications running on the virtual machine by matching the at least one first application signature with one or more second application signatures previously stored.

Abstract: Determining a characteristic of a configuration file that is used to discover configuration files in a target machine, a computer identifies, using information associated with a configuration item of a machine, a candidate configuration file related to the configuration item of the machine, from among a plurality of files from the machine. The computer extracts a value of a feature of the candidate configuration file and aggregates the candidate configuration file with a second candidate configuration file related to the same configuration item identified from among a plurality of files from a second machine, based on the extracted value. The computer then determines a configuration file related to the configuration item from among the aggregated candidate configuration files based on a result of the aggregation, and determines a characteristic of the configuration file related to the configuration item.

Abstract: Embodiments facilitating performance anomaly detection are described. A computer-implemented method comprises: detecting, by a device operatively coupled to one or more processing units, based on monitoring data of a plurality of performance metrics of a monitored device, at least one trend within the monitoring data of the respective performance metrics; removing, by the device, the at least one trend from the monitoring data of the respective performance metrics to generate modified data of the respective performance metrics; and detecting, by the device, a performance anomaly based on the modified data of the respective performance metrics and a behavior clustering model comprising at least one steady state.