The Vision of Autonomic Computing
Computer
ECOOP '01 Proceedings of the 15th European Conference on Object-Oriented Programming
A framework for requirents monitoring of service based systems
Proceedings of the 2nd international conference on Service oriented computing
A scenario based notation for specifying temporal properties
Proceedings of the 2006 international workshop on Scenarios and state machines: models, algorithms, and tools
Run-Time Monitoring of Instances and Classes of Web Service Compositions
ICWS '06 Proceedings of the IEEE International Conference on Web Services
AO4BPEL: An Aspect-oriented Extension to BPEL
World Wide Web
Non-intrusive monitoring and service adaptation for WS-BPEL
Proceedings of the 17th international conference on World Wide Web
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Runtime monitoring composite web services through stateful aspect extension
Journal of Computer Science and Technology
A planning approach for message-oriented semantic web service composition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Policies and aspects for the supervision of BPEL processes
CAiSE'07 Proceedings of the 19th international conference on Advanced information systems engineering
Isolating process-level concerns using padus
BPM'06 Proceedings of the 4th international conference on Business Process Management
Towards dynamic monitoring of WS-BPEL processes
ICSOC'05 Proceedings of the Third international conference on Service-Oriented Computing
Runtime verification of service-oriented systems: a well-rounded survey
International Journal of Web and Grid Services
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To achieve self-healing web services composition, much work has been studied in the area of web services composition recently. However, most work addresses the problem of runtime monitoring, diagnosis and recovery in isolation. What is missing, however, is a unified solution that can be used to tackle this challenge in a principled manner. This paper presents a fresh view on self-healing web services composition. In particular, rather than building baseline system model a priori, we advocate using statistical learning theory(SLT) technique to extract it by observing the behavior of web services composition and locate the potential anomaly.