Assisted mashup development: On the discovery and recommendation of mashup composition knowledge

Carlos Rodríguez, Soudip Roy Chowdhury, Florian Daniel, Hamid R Motahari Nezhad, Fabio Casati

Результат исследований: Материалы для книги/типы отчетовГлава

4 Цитирования (Scopus)

Аннотация

Over the past few years, mashup development has been made more accessible with tools such as Yahoo! Pipes that help in making the development task simpler through simplifying technologies. However, mashup development is still a difficult task that requires knowledge about the functionality of web APIs, parameter settings, data mappings, among other development efforts. In this work, we aim at assisting users in the mashup process by recommending development knowledge that comes in the form of reusable composition knowledge. This composition knowledge is harvested from a repository of existing mashup models by mining a set of composition patterns, which are then used for interactively providing composition recommendations while developing the mashup. When the user accepts a recommendation, it is automatically woven into the partial mashup model by applying modeling actions as if they were performed by the user. In order to demonstrate our approach we have implemented Baya, a Firefox plugin for Yahoo! Pipes that shows that it is indeed possible to harvest useful composition patterns from existing mashups, and that we are able to provide complex recommendations that can be automatically woven inside Yahoo! Pipes' web-based mashup editor.

Язык оригиналаАнглийский
Название основной публикацииWeb Services Foundations
ИздательSpringer New York
Страницы683-708
Число страниц26
Том9781461475187
ISBN (электронное издание)9781461475187
ISBN (печатное издание)1461475171, 9781461475170
DOI
СостояниеОпубликовано - 1 окт 2014
Опубликовано для внешнего пользованияДа

ASJC Scopus subject areas

  • Computer Science(all)

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  • Цитировать

    Rodríguez, C., Chowdhury, S. R., Daniel, F., Nezhad, H. R. M., & Casati, F. (2014). Assisted mashup development: On the discovery and recommendation of mashup composition knowledge. В Web Services Foundations (Том 9781461475187, стр. 683-708). Springer New York. https://doi.org/10.1007/978-1-4614-7518-7_27