Presentation-oriented mashup applications are usually developed by manual selection and assembly of pre-existent components. The latter are either described on a very technical, functional level, or using informal descriptors, such as tags, which bear certain ambiguities. With regard to the increasing number and complexity of available components, their discovery and integration has become a challenge for non-programmers. Therefore, we present a novel concept for the task-based recommendation of mashup components, which comprises a more natural, task-driven description of user requirements and a corresponding semantic matching algorithm for universal mashup components. By its realization and integration with an composition platform, we could prove the feasibility and sufficiency of our approach. © 2012 Springer-Verlag.
CITATION STYLE
Tietz, V., Blichmann, G., Pietschmann, S., & Meißner, K. (2012). Task-based recommendation of mashup components. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7059 LNCS, pp. 25–36). https://doi.org/10.1007/978-3-642-27997-3_3
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