International Business Machines Corporation

Andres Omar Portillo Dominguez

Hitham Ahmed Assem Aly Salama

Patrick Joseph O'Sullivan

Peter Francis Sweeney

Erik Richter Altman

Nicholas Matthew Mitchell

Faisal Ghaffar

Teodora Sandra Buda

Bora Caglayan

Shane Brady

Magdalena Kacmajor

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.

Abnormal event analysis

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.

Dynamically identifying performance anti-patterns

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.

Dynamically adapting a test workload to accelerate the identification of performance issues

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.

Rule-based adaptive monitoring of application performance

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Dublin, Ireland

Andres Omar Portillo Dominguez