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Cited by 871

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Adaptive Service Composition in Flexible Processes

Ardagna, D., Pernici, B.·2007·IEEE Transactions on Software Engineering

Abstract

In advanced service oriented systems, complex applications, described as abstract business processes, can be executed by invoking a number of available Web services. End users can specify different preferences and constraints and service selection can be performed dynamically identifying the best set of services available at runtime. In this paper, we introduce a new modeling approach to the Web service selection problem that is particularly effective for large processes and when QoS constraints are severe. In the model, the Web service selection problem is formalized as a mixed integer linear programming problem, loops peeling is adopted in the optimization, and constraints posed by stateful Web services are considered. Moreover, negotiation techniques are exploited to identify a feasible solution of the problem, if one does not exist. Experimental results compare our method with other solutions proposed in the literature and demonstrate the effectiveness of our approach toward the identification of an optimal solution to the QoS constrained Web service selection problem.

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Metadata

Authors
Ardagna, D., & Pernici, B.
Venue
IEEE Transactions on Software Engineering
Published
2007-06
Volume
33
Issue
6
Pages
369-384
ISSN
0098-5589 (ISSN-L)
OpenAlex
W2127414233
Cited by
871
References
18
FWCI
161.50
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Adaptive Service Composition in Flexible Processes. https://app.cochat.ai/papers/10.1109/tse.2007.1011
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Ardagna, D., & Pernici, B. (2007). Adaptive Service Composition in Flexible Processes. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2007.1011

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.