Abstract: A system (200) comprises a plurality of data collectors (210), a correlator (220), a context analyser (230), a baseline analyser (250), a database (260), and a graphical user interface (GUI) (270). The data collectors (210) are deployed on the services or applications that they monitor, or on the network between these applications as a network appliance, and are designed to capture messages that are passed between the various services. The data collectors (210) are non-intrusive, i.e. they do not to impact the behavior of the monitored services. The data collectors (210) can capture messages transmitted using communication protocols including, but not limited to, SOAP, XML, HTTP, JMS, MSMQ, and the like.

Abstract: A method and apparatus that uses application errors as a predictive metric for overall measuring of applications functional health are disclosed. The automated system intercepts messages exchanged between inter-services of enterprise applications, analyzes the context of those messages, and automatically derives application errors embedded in the message. Thereafter, it is capable of showing deviations from expected behavior for the purposes of predicting failures of the monitored application. Furthermore, the invention displays the user's real-time actionable data generated using the application errors.

Abstract: According to the invention, a data model and method and apparatus for performing content and context modeling are disclosed. The method dynamically classifies and gathers selective information on various monitored systems to detect content related problems and provide context for diagnosing the root cause of these problems. The selected, monitored information for classification is converted to a plurality of dimensions that may be preconfigured, added incrementally after the monitored system is in production, or when a need for more advanced analysis or for wider context arise.

Abstract: A method and apparatus for detecting abnormal behavior of enterprise software applications is disclosed. A profile that represents the behavior of the function is created for each service and error function integrated in an enterprise software application. This profile is based on input measurements, such as response time, throughput, and non-availability. For each such input measurement, the expected behavior is determined, as well as the upper and lower bounds on that expected behavior. The invention further monitors the behavior of service and error functions and produces an exception if at least one of the upper or lower bounds is violated. The detection scheme disclosed is dynamic, adaptive, and has self-learning capabilities.