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Traditional problem determination techniques rely on static dependency models that are difficult to generate accurately in today's large, distributed, and dynamic application environments such as e-commerce systems. In this paper, the authors present a dynamic analysis methodology that automates problem determination in these environments by coarse-grained tagging of numerous real client requests as they travel through the system and using data mining techniques to correlate the believed failures and successes of these requests to determine which components are most likely to be at fault. To validate the methodology, they have implemented Pinpoint, a framework for root cause analysis on the J2EE platform that requires no knowledge of the application components.
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