Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Pinpoint: Problem Determination in Large, Dynamic Internet Services
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Magpie: online modelling and performance-aware systems
HOTOS'03 Proceedings of the 9th conference on Hot Topics in Operating Systems - Volume 9
Path-based faliure and evolution management
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Why do internet services fail, and what can be done about it?
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
Detecting application-level failures in component-based Internet services
IEEE Transactions on Neural Networks
Web analytics and the art of data summarization
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
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Web applications suffer from poor reliability. Practitioners commonly rely on fast failure detection to recover their applications quickly to reduce the effects of the failures on other users. In this paper, we present a technique for detecting user-visible failures by analyzing Web logs. Our technique applies a first-order Markov model to infer anomalous browsing behavior discovered in Web logs as indicators that users have encountered failures. We implemented our technique in a tool called REBA (REcursive Byesian Analysis of Web Logs). We evaluated our technique using REBA applied to the Web site of NASA. The results demonstrate that our technique can detect user-visible failures with reasonable cost.