Abstract: A method, computer system, and a computer program product for data compression is provided. The present invention may include receiving operational data. The present invention may include profiling the operational data using one or more normalizing functions. The present invention may include extracting a plurality of patterns from the operational data. The present invention may include compressing the operational data based on the plurality of patterns extracted.

Abstract: Embodiments of the invention are directed to annotating a log based on processing log documentation. Aspects include obtaining the data set and obtaining a set of templates, wherein one or more template of the set of templates includes a variable labeled as the data type. Aspects also include annotating the data set based on the set of templates, wherein the annotating includes labeling each value of the variable in the data set with the data type. Aspects further include creating an updated data set by replacing each value of the variable in the data set that is labeled with the data type with a new data value.

Abstract: A method, computer system, and a computer program product for application modernization is provided. The present invention may include receiving operation data related to an application. The present invention may include identifying a plurality of entities based on the operation data related to the application. The present invention may include identifying one or more interactions between each of the plurality of entities. The present invention may include determining an energy consumption pattern for the application by analyzing a consumption of energy related to workload. The present invention may include determining a modernization scope for the application based on the energy consumption pattern.

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: Embodiments of the present invention relate to methods, systems, and computer program products for event management. In a method, a plurality of notes that are comprised in a plurality of event records are obtained in a computer system. A plurality of paragraphs that are comprised in the plurality of notes are classified into a plurality of content types based on a content analysis of the plurality of paragraphs. The plurality of notes are classified into a plurality of semantic types based on the plurality of content types and a syntactic parsing to the plurality of notes. A knowledge item is generated for managing an event in the computer system based on a group of notes in the plurality of notes that are classified into the plurality of semantic types. With these embodiments, knowledge items for managing events may be obtained in an easier and more effective way.

Abstract: Embodiments of the invention are directed to annotating a log based on processing log documentation. Aspects include obtaining the log having a plurality of entries. Aspects also include creating a set of log entry templates by processing the log documentation associated with the log, wherein each log entry template includes one or more constants and one or more variables. Aspects further include annotating each of the plurality of entries based on the set of templates, wherein the annotating includes labeling each value of the one or more variables with a variable name.

Abstract: Embodiments of the present invention relate to methods, systems, and computer program products for event management. In a method, a plurality of notes that are comprised in a plurality of event records are obtained in a computer system. A plurality of paragraphs that are comprised in the plurality of notes are classified into a plurality of content types based on a content analysis of the plurality of paragraphs. The plurality of notes are classified into a plurality of semantic types based on the plurality of content types and a syntactic parsing to the plurality of notes. A knowledge item is generated for managing an event in the computer system based on a group of notes in the plurality of notes that are classified into the plurality of semantic types. With these embodiments, knowledge items for managing events may be obtained in an easier and more effective way.

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: Embodiments of the invention are directed to annotating a log based on processing log documentation. Aspects include obtaining the data set and obtaining a set of templates, wherein one or more template of the set of templates includes a variable labeled as the data type. Aspects also include annotating the data set based on the set of templates, wherein the annotating includes labeling each value of the variable in the data set with the data type. Aspects further include creating an updated data set by replacing each value of the variable in the data set that is labeled with the data type with a new data value.

Abstract: Embodiments of the invention are directed to annotating a log based on processing log documentation. Aspects include obtaining the log having a plurality of entries. Aspects also include creating a set of log entry templates by processing the log documentation associated with the log, wherein each log entry template includes one or more constants and one or more variables. Aspects further include annotating each of the plurality of entries based on the set of templates, wherein the annotating includes labeling each value of the one or more variables with a variable name.

Abstract: According to an aspect, a method includes searching for a correlated log identifier in a correlation database based on detecting a metrics-based anomaly. The method also includes providing, in a problem diagnosis, related log information associated with the correlated log identifier based on locating one or more log entries including the correlated log identifier in a same time window as the metrics-based anomaly. The method further includes searching for a correlated metric in the correlation database based on detecting a log-based anomaly and providing, in the problem diagnosis, related metric information associated with the correlated metric based on locating one or more metrics records including the correlated metric in the same time window as the log-based anomaly.

Abstract: A computer-implemented method is presented for automatically generating alerting rules. The method includes identifying, via offline analytics, abnormal patterns and normal patterns from history logs based on machine learning, statistical analysis and deep learning, the history logs stored in a history log database, automatically generating the alerting rules based on the identified abnormal and normal patterns, and transmitting the alerting rules to an alerting engine for evaluation. The method further includes receiving a plurality of online log messages from a plurality of computing devices connected to a network, augmenting the plurality of online log messages, and extracting information from the plurality of augmented online log messages to be provided to the alerting engine, the alerting engine configured to approve and enforce the alerting rules automatically generated by the offline analytics processing.

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: 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: 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: A computer-implemented method is presented for detecting anomalies in dynamic datasets generated in a cloud computing environment. The method includes monitoring a plurality of cloud servers receiving a plurality of data points, employing a two-level clustering training module to generate micro-clusters from the plurality of data points, each of the micro-clusters representing a set of original data from the plurality of data points, employing a detecting module to detect normal data points, abnormal data points, and unknown data points from the plurality of data points via a detection model, employing an evolving module using a different evolving mechanism for each of the normal, abnormal, and unknown data points to evolve the detection model, and generating a system report displayed on a user interface, the system report summarizing the micro-cluster information.

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: A method, computer system, and a computer program product are provided for dynamic viewing of an operation graph depicting a distributed workload in an Information Technology system. A list may be received as input and may include selected nodes. A sub-graph may be abstracted from a global graph. The sub-graph and the global graph may depict interactions among ops objects of the distributed workload using nodes and edges. First nodes may be removed from the global graph according to the list. Direct edges between remaining nodes from the global graph may be maintained. A first indirect edge may be generated based on the removed first nodes. The first indirect edge may run between a first remaining node and a second remaining node. The sub-graph may be presented removed from the global graph.

Abstract: According to an aspect, a method includes searching for a correlated log identifier in a correlation database based on detecting a metrics-based anomaly. The method also includes providing, in a problem diagnosis, related log information associated with the correlated log identifier based on locating one or more log entries including the correlated log identifier in a same time window as the metrics-based anomaly. The method further includes searching for a correlated metric in the correlation database based on detecting a log-based anomaly and providing, in the problem diagnosis, related metric information associated with the correlated metric based on locating one or more metrics records including the correlated metric in the same time window as the log-based anomaly.