Abstract: A trained classification model is executed, causing a classification of a first set of file system usage data into a set of categories comprising a trend category and a periodicity category. Responsive to the first set of file system usage data being classified into the trend category, a time series of the first set of file system usage data is generated. Responsive to the first set of file system usage data being classified into the periodicity category, using an anomaly detection model, an anomaly within the first set of file system usage data is detected. Responsive to predicting that the time series will exceed a threshold, a first reconfiguring of a file system resource is caused, altering a capacity of the file system. Responsive to detecting the anomaly, a second reconfiguring of the file system resource is caused, altering a capacity of the file system.

Abstract: A processor is configured to create a log data specific lexicon wherein each word in the log data specific lexicon corresponds to a weighted sentiment score with a binary polarity. The processor is further configured to assign a sentiment value to a log message based on the weighted sentiment scores of words appearing in the log message. The processor may further determine an alert type for the log message based on the sentiment value, a class indicating an issue the log message, and a priority of the log message, where the alert type is preconfigured with a set of alert type values of varying risk levels and wherein a system alert contains a predefined set of key performance indicators corresponding to the alert type.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: creating a log data specific lexicon based on log data samples, each word in the log data specific lexicon corresponding to a weighted sentiment score with a binary polarity. A log message is obtained, and a sentiment value of the log message is assigned based on respective weighted sentiment scores of words appearing in the log message. The log message is classified for a class indicating an issue the log message addresses. An alert type for the log message is determined based on the sentiment value, the class, and a priority of the log message, and a system alert including a set of key performance indicators according to the alert type is produced to a user.

Abstract: Systems, methods, and computer programming products for predicting, preventing and remediating failures of batch jobs being executed and/or queued for processing at future scheduled time. Batch job parameters, messages and system logs are stored in knowledge bases and/or inputted into AI models for analysis. Using predictive analytics and/or machine learning, batch job failures are predicted before the failures occur. Mappings of processes used by each batch job, historical data from previous batch jobs and data identifying the success or failure thereof, builds an archive that can be refined over time through active learning feedback and AI modeling to predictively recommend actions that have historically prevented or remediated failures from occurring. Recommended actions are reported to the system administrator or automatically applied. As job failures occur over time, mappings of the current system log to logs for the unsuccessful batch jobs help the root cause analysis becomes simpler and more automated.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: creating a log data specific lexicon based on log data samples, each word in the log data specific lexicon corresponding to a weighted sentiment score with a binary polarity. A log message is obtained, and a sentiment value of the log message is assigned based on respective weighted sentiment scores of words appearing in the log message. The log message is classified for a class indicating an issue the log message addresses. An alert type for the log message is determined based on the sentiment value, the class, and a priority of the log message, and a system alert including a set of key performance indicators according to the alert type is produced to a user.

Abstract: Systems, methods, and computer programming products for predicting, preventing and remediating failures of batch jobs being executed and/or queued for processing at future scheduled time. Batch job parameters, messages and system logs are stored in knowledge bases and/or inputted into AI models for analysis. Using predictive analytics and/or machine learning, batch job failures are predicted before the failures occur. Mappings of processes used by each batch job, historical data from previous batch jobs and data identifying the success or failure thereof, builds an archive that can be refined over time through active learning feedback and AI modeling to predictively recommend actions that have historically prevented or remediated failures from occurring. Recommended actions are reported to the system administrator or automatically applied. As job failures occur over time, mappings of the current system log to logs for the unsuccessful batch jobs help the root cause analysis becomes simpler and more automated.

Abstract: A trained classification model is executed, causing a classification of a first set of file system usage data into a set of categories comprising a trend category and a periodicity category. Responsive to the first set of file system usage data being classified into the trend category, a time series of the first set of file system usage data is generated. Responsive to the first set of file system usage data being classified into the periodicity category, using an anomaly detection model, an anomaly within the first set of file system usage data is detected. Responsive to predicting that the time series will exceed a threshold, a first reconfiguring of a file system resource is caused, altering a capacity of the file system. Responsive to detecting the anomaly, a second reconfiguring of the file system resource is caused, altering a capacity of the file system.

Abstract: A context-sensitive help system for obtaining insights from a target environment in an unobtrusive manner. The context-sensitive help system detects a search or opening of a help document containing a plurality of placeholders and product/component names by a user; crawls documents in the knowledgebase and extracts product and/or component names and corresponding configuration parameters or placeholders; stores the product and/or component names and placeholders in the placeholder dictionary along with links and an index to documents in the knowledgebase. The system extracts insights from the target environment in an unobtrusive manner; and replaces placeholders in the help document with values from the target environment based on the insights from the target environment; and then displays the help document to the user.

Abstract: A context-sensitive help system for obtaining insights from a target environment in an unobtrusive manner. The context-sensitive help system detects a search or opening of a help document containing a plurality of placeholders and product/component names by a user; crawls documents in the knowledgebase and extracts product and/or component names and corresponding configuration parameters or placeholders; stores the product and/or component names and placeholders in the placeholder dictionary along with links and an index to documents in the knowledgebase. The system extracts insights from the target environment in an unobtrusive manner; and replaces placeholders in the help document with values from the target environment based on the insights from the target environment; and then displays the help document to the user.

Abstract: A context-sensitive help system for obtaining insights from a target environment in an unobtrusive manner. The context-sensitive help system detects a search or opening of a help document containing a plurality of placeholders and product/component names by a user; crawls documents in the knowledgebase and extracts product and/or component names and corresponding configuration parameters or placeholders; stores the product and/or component names and placeholders in the placeholder dictionary along with links and an index to documents in the knowledgebase. The system extracts insights from the target environment in an unobtrusive manner; and replaces placeholders in the help document with values from the target environment based on the insights from the target environment; and then displays the help document to the user.

Abstract: A context-sensitive help system for obtaining insights from a target environment in an unobtrusive manner. The context-sensitive help system detects a search or opening of a help document containing a plurality of placeholders and product/component names by a user; crawls documents in the knowledgebase and extracts product and/or component names and corresponding configuration parameters or placeholders; stores the product and/or component names and placeholders in the placeholder dictionary along with links and an index to documents in the knowledgebase. The system extracts insights from the target environment in an unobtrusive manner; and replaces placeholders in the help document with values from the target environment based on the insights from the target environment; and then displays the help document to the user.