Abstract
When composing mashups, the selection of suitable services is mainly based on functional requirements and does not consider the quality of the single services. In this paper, we show that the quality of component services can drive the production of recommendations that can help building quality mashups. We capitalize on a quality model for mashup services and discuss the concept of mashability, a multi-dimension quality property that expresses the capability of a component to maximize the quality of a mashup, and the concept of role-based composition quality, i.e., the quality of mashup compositions weighted according to specific roles that the composed services play within the mashup. We then show how such concepts can enable the production of quality-based recommendations for the mashup design. © 2010 Springer-Verlag.
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CITATION STYLE
Picozzi, M., Rodolfi, M., Cappiello, C., & Matera, M. (2010). Quality-based recommendations for mashup composition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6385 LNCS, pp. 360–371). https://doi.org/10.1007/978-3-642-16985-4_32
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