Original language | English |
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Title of host publication | Service-Oriented System Engineering (SOSE), 2015 IEEE Symposium on |
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Publisher | IEEE |
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Pages | 40-49 |
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Number of pages | 10 |
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Volume | 1 |
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ISBN (Print) | 9781479983575 |
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DOIs | |
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Published | Apr 2015 |
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Event | 2015 IEEE Symposium on Service-Oriented System Engineering (SOSE 2015) - San Francisco Bay, United States Duration: 30 Mar 2015 → 3 Apr 2015 |
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Conference | 2015 IEEE Symposium on Service-Oriented System Engineering (SOSE 2015) |
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Country/Territory | United States |
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City | San Francisco Bay |
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Period | 30/03/2015 → 3/04/2015 |
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Provenance awareness adds a new dimension to SOC systems' design allowing them to answer different kinds of questions about the system's history processing by recording information during system execution. Incorporating provenance in SOC systems requires to carefully model and analyse provenance infrastructure requirements at system's design time in order to cope with the inherent complexity encapsulated into the SOC systems' execution cycle, considering the aspects of service discovery, selection and dynamic composition. Formally specifying provenance-infrastructure behavior assists in the design/analysis of the system's accountability by introducing provenance mechanisms to collect the respective provenance where the latter can be verified and revised beforehand the system's actual implementation. We have previously presented a data model to express the graph-based structure of the provenance data collected for composite services. In this paper we focus on a template provenance-infrastructure model specifying the behavior required in order to issue the respective provenance recording calls through the SOC execution cycle. We then bind the infrastructure design to specific SOC models in order to analyze whether this leads to actual recording of the provenance required for answering provenance queries. We accomplish this by proposing a generic algorithm that simulates the provenance-infrastructure behavior and generates sample provenance-data graphs.