Abstract: An embodiment for contextualizing abnormal events which employ location-based social networks, LBSN, data to determine events that may be linked to the abnormal events is provided. The embodiment may include detecting an occurrence of an abnormal event within a geographic region, wherein the abnormal event occurs at an occurrence time. The embodiment may also include obtaining location-based social networks, LBSN, data relating to the geographic region for a time period including the occurrence time. The embodiment may further include analyzing the obtained LBSN data, wherein the analyzation determines a linked event within the geographic region for the time period. The embodiment may also include associating the linked event with the abnormal event.

Abstract: An embodiment for contextualizing abnormal events which employ location-based social networks, LBSN, data to determine events that may be linked to the abnormal events is provided. The embodiment may include detecting an occurrence of an abnormal event within a geographic region, wherein the abnormal event occurs at an occurrence time. The embodiment may also include obtaining location-based social networks, LBSN, data relating to the geographic region for a time period including the occurrence time. The embodiment may further include analyzing the obtained LBSN data, wherein the analyzation determines a linked event within the geographic region for the time period. The embodiment may also include associating the linked event with the abnormal event.

Abstract: Dynamically identifying performance anti-patterns in a software system is based on a set of documented symptoms that are evaluated in real-time. The evaluation is based on the observed system behavior and its comparison against the documented symptoms of different types of performance issues.

Abstract: An improvement to the process for identifying software problems in performance testing is achieved by dynamically adjusting workloads in real-time to stress the functionality of an application suspected of causing a software problem.

Abstract: A method for dynamically and adaptively monitoring a system based on its running behavior adjusts monitoring levels of the monitored application in real-time. A rules-based mechanism dynamically adjusts monitoring levels in real-time, based on the system's performance observed during a workload run, whether in a production or test environment.

Abstract: Dynamically identifying performance anti-patterns in a software system is based on a set of documented symptoms that are evaluated in real-time. The evaluation is based on the observed system behavior and its comparison against the documented symptoms of different types of performance issues.

Abstract: Dynamically identifying performance anti-patterns in a software system is based on a set of documented symptoms that are evaluated in real-time. The evaluation is based on the observed system behavior and its comparison against the documented symptoms of different types of performance issues.

Abstract: Dynamically identifying performance anti-patterns in a software system is based on a set of documented symptoms that are evaluated in real-time. The evaluation is based on the observed system behavior and its comparison against the documented symptoms of different types of performance issues.

Abstract: An improvement to the process for identifying software problems in performance testing is achieved by dynamically adjusting workloads in real-time to stress the functionality of an application suspected of causing a software problem.

Abstract: A method for dynamically and adaptively monitoring a system based on its running behavior adjusts monitoring levels of the monitored application in real-time. A rules-based mechanism dynamically adjusts monitoring levels in real-time, based on the system's performance observed during a workload run, whether in a production or test environment.