Abstract: A method for archiving monitoring data by enabling real-time analysis within a live database. A processor receives a set of parameters, which is selected based on monitoring data of a system application. The processor determines a historical schema of monitoring data and a retention period for a current schema of monitoring data, based on the set of parameters. The processor performs an impact analysis of the historical schema and retention period of the monitoring data. Upon acceptance of the impact analysis, the processor generates the historical schema which is applied to a table of monitoring data, populated by copying monitoring data from the current schema to the historical schema, within the live database. The processor removes monitoring data exceeding the rolling retention period from both the current schema and historical schema, based on the rolling retention period of the current schema.

Abstract: A method for archiving monitoring data by enabling real-time analysis within a live database. A processor receives a set of parameters, which is selected based on monitoring data of a system application. The processor determines a historical schema of monitoring data and a retention period for a current schema of monitoring data, based on the set of parameters. The processor performs an impact analysis of the historical schema and retention period of the monitoring data. Upon acceptance of the impact analysis, the processor generates the historical schema which is applied to a table of monitoring data, populated by copying monitoring data from the current schema to the historical schema, within the live database. The processor removes monitoring data exceeding the rolling retention period from both the current schema and historical schema, based on the rolling retention period of the current schema.

Abstract: A method for archiving monitoring data by enabling real-time analysis within a live database. A processor receives a set of parameters, which is selected based on monitoring data of a system application. The processor determines a historical schema of monitoring data and a retention period for a current schema of monitoring data, based on the set of parameters. The processor performs an impact analysis of the historical schema and retention period of the monitoring data. Upon acceptance of the impact analysis, the processor generates the historical schema which is applied to a table of monitoring data, populated by copying monitoring data from the current schema to the historical schema, within the live database. The processor removes monitoring data exceeding the rolling retention period from both the current schema and historical schema, based on the rolling retention period of the current schema.

Abstract: A method for archiving monitoring data by enabling real-time analysis within a live database. A processor receives a set of parameters, which is selected based on monitoring data of a system application. The processor determines a historical schema of monitoring data and a retention period for a current schema of monitoring data, based on the set of parameters. The processor performs an impact analysis of the historical schema and retention period of the monitoring data. Upon acceptance of the impact analysis, the processor generates the historical schema which is applied to a table of monitoring data, populated by copying monitoring data from the current schema to the historical schema, within the live database. The processor removes monitoring data exceeding the rolling retention period from both the current schema and historical schema, based on the rolling retention period of the current schema.

Abstract: Reducing resource requirements in an instrumented process tracing system, a process having a top instrumented process and a nested hierarchy of instrumented sub-processes. A computer receives a plurality of instrumented process data from the top process and the sub-processes, each datum including a process identifier, a process type, a top process identifier, and a process completion elapsed time. Based on the computer determining that the process identifier and the top process identifier in the datum received are equivalent: if the process completion elapsed time in the datum received is determined to be less than a threshold value, the computer writes a summary of the plurality of instrumented process data to a data store, and if the process completion elapsed time in the datum received is determined to not be less than the threshold value, the computer writes the plurality of instrumented process data to the data store.

Abstract: Reducing resource requirements in an instrumented process tracing system, a process having a top instrumented process and a nested hierarchy of instrumented sub-processes. A computer receives a plurality of instrumented process data from the top process and the sub-processes, each datum including a process identifier, a process type, a top process identifier, and a process completion elapsed time. Based on the computer determining that the process identifier and the top process identifier in the datum received are equivalent: if the process completion elapsed time in the datum received is determined to be less than a threshold value, the computer writes a summary of the plurality of instrumented process data to a data store, and if the process completion elapsed time in the datum received is determined to not be less than the threshold value, the computer writes the plurality of instrumented process data to the data store.

Abstract: A data warehouse solution system comprises a metadata model, a user interface and an engine. The metadata model has an information needs model including metadata regarding information needs for building reports by users, and a data information model including metadata describing data that is available for building reports. The user interface has a customer user interface for presenting the information needs model to the users for report generation, and a modeling user interface for presenting the data information model to the users for manipulating data warehouse objects. The engine has a report management service unit for providing report management service using the information needs model, and a data management service unit for providing data management service including generation of a data warehouse using the data information model.

Abstract: A data warehouse solution system comprises a metadata model, a user interface and an engine. The metadata model has an information needs model including metadata regarding information needs for building reports by users, and a data information model including metadata describing data that is available for building reports. The user interface has a customer user interface for presenting the information needs model to the users for report generation, and a modeling user interface for presenting the data information model to the users for manipulating data warehouse objects. The engine has a report management service unit for providing report management service using the information needs model, and a data management service unit for providing data management service including generation of a data warehouse using the data information model.