Please use this identifier to cite or link to this item:

http://hdl.handle.net/10609/78207
Title: A UML profile for OData web APIs
Author: Ed-douibi, Hamza
Cánovas Izquierdo, Javier Luis  
Cabot Sagrera, Jordi  
Others: Universitat Oberta de Catalunya (UOC)
Keywords: UML
OData
web API
Issue Date: 1-Jun-2017
Publisher: Lecture Notes in Computer Science
Citation: Ed-douibi, H., Cánovas Izquierdo, J.L. & Cabot, J. (2017). A UML Profile for OData Web APIs. Lecture Notes in Computer Science, 10360(), 420-428. doi: 10.1007/978-3-319-60131-1_28
Also see: https://doi.org/10.1007/978-3-319-60131-1_28
Abstract: More and more individuals and organizations are making their data available online publicly, resulting in a growing market of technologies and services to help consume data and extract its real value. One of the several ways to publish data on the Web is via Web APIs. Unlike other approaches like RDF, Web APIs provide a simple way to query structured data by relying only on the HTTP protocol. Standards and frameworks such as Open API or API Blueprint offer a way to create Web APIs but OData stands out from the rest as it is specifically tailored to deal with data sources. However, creating an OData Web API is a hard and time-consuming task for data providers as they have to choose between relying on commercial solutions, which are heavy and require a deep knowledge of their corresponding platforms, or create a customized solution to share their data. We propose an approach that leverages on model-driven techniques to facilitate the development of OData Web APIs. The approach relies on a UML profile for OData allowing to annotate a UML class diagram with OData stereotypes. In this paper we describe the profile and show how class diagrams can be automatically annotated with such profile.
Language: English
URI: http://hdl.handle.net/10609/78207
ISSN: 0302-9743MIAR
Appears in Collections:Articles
Articles

Share:
Export:
Files in This Item:
File Description SizeFormat 
Ed-Douibi_et_al_ A_UML_Profile_for_OData_Web_APIs_preprint.pdf483.28 kBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons