Mashup is gaining wide popularity as an opportunity for building short-living, situational applications, by selecting from-the-shelf available composable elements from different sources. We present a recommendation system, called SMASHAKER (Semantic MASHup shAKER), that proactively supports the designer in the selection of mashup components. In particular, SMASHAKER mitigates the burden of manual composition, that is hampered by the semantic heterogeneity of component descriptions (in terms of I/O variables, operations, events) and by the variety of elements to be combined, ranging from traditional data sources to application logics (e.g., Web services) and data feeds (e.g., RSS/Atom feeds). Semantic Web technologies are applied to describe and organize components and to propose and rank them with respect to their similarity with designer's requirements and the degree of coupling among them. The mashup designer interacts with our system, which exploiting the semantic description and organization of mashup components recommends step-by-step suitable selection according to similarity and coupling metrics.
A semantic framework for mashup composition
BIANCHINI, Devis;DE ANTONELLIS, Valeria;MELCHIORI, Michele
2010-01-01
Abstract
Mashup is gaining wide popularity as an opportunity for building short-living, situational applications, by selecting from-the-shelf available composable elements from different sources. We present a recommendation system, called SMASHAKER (Semantic MASHup shAKER), that proactively supports the designer in the selection of mashup components. In particular, SMASHAKER mitigates the burden of manual composition, that is hampered by the semantic heterogeneity of component descriptions (in terms of I/O variables, operations, events) and by the variety of elements to be combined, ranging from traditional data sources to application logics (e.g., Web services) and data feeds (e.g., RSS/Atom feeds). Semantic Web technologies are applied to describe and organize components and to propose and rank them with respect to their similarity with designer's requirements and the degree of coupling among them. The mashup designer interacts with our system, which exploiting the semantic description and organization of mashup components recommends step-by-step suitable selection according to similarity and coupling metrics.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
