Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Web Services Foundations |
| Publisher | Springer New York |
| Pages | 683-708 |
| Number of pages | 26 |
| Volume | 9781461475187 |
| ISBN (Electronic) | 9781461475187 |
| ISBN (Print) | 1461475171, 9781461475170 |
| DOIs | |
| Publication status | Published - 1 Oct 2014 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Computer Science(all)
Cite this
Assisted mashup development : On the discovery and recommendation of mashup composition knowledge. / Rodríguez, Carlos; Chowdhury, Soudip Roy; Daniel, Florian; Nezhad, Hamid R Motahari; Casati, Fabio.
Web Services Foundations. Vol. 9781461475187 Springer New York, 2014. p. 683-708.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Assisted mashup development
T2 - On the discovery and recommendation of mashup composition knowledge
AU - Rodríguez, Carlos
AU - Chowdhury, Soudip Roy
AU - Daniel, Florian
AU - Nezhad, Hamid R Motahari
AU - Casati, Fabio
PY - 2014/10/1
Y1 - 2014/10/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84906746581&partnerID=8YFLogxK
U2 - 10.1007/978-1-4614-7518-7_27
DO - 10.1007/978-1-4614-7518-7_27
M3 - Chapter
AN - SCOPUS:84906746581
SN - 1461475171
SN - 9781461475170
VL - 9781461475187
SP - 683
EP - 708
BT - Web Services Foundations
PB - Springer New York
ER -