A theory of diagnosis from first principles
Artificial Intelligence
Enhancing Web Services with Diagnostic Capabilities
ECOWS '05 Proceedings of the Third European Conference on Web Services
MonitoringWeb Service Networks in a Model-based Approach
ECOWS '05 Proceedings of the Third European Conference on Web Services
A framework for decentralized qualitative model-based diagnosis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Scalable diagnosability checking of event-driven systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Formal verification of diagnosability via symbolic model checking
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Diagnosability Analysis Based on Component-Supported Analytical Redundancy Relations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Minimizing test-point allocation to improve diagnosability in business process models
Journal of Systems and Software
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In this paper we deal with the problem of model-based diagnosability analysis for Web Services. The goal of diagnosability analysis is to determine whether the information one can observe during service execution is sufficient to precisely locate (by means of diagnostic reasoning) the source of the problem. The major difficulty in the context of Web Services is that models are distributed and no single entity has a global view of the complete model. In the paper we propose an approach that computes diagnosability for the decentralized diagnostic framework, described in [1], based on a Supervisor coordinating several Local Diagnosers. We also show that diagnosability analysis can be performed without requiring the Local Diagnosers different operations than those needed for diagnosis. The proposed approach is incremental: each fault is first analyzed independently of the occurrence of other faults, then the results are used to analyze combinations of behavioral modes, avoiding in most cases an exhaustive check of all combinations.