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RESTful API Modeling Language (RAML) makes it easy to manage the whole API lifecycle from design to sharing. It's concise - you only write what you need to define - and reusable. It is machine readable API design that is actually human friendly.
A query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.
Representational State Transfer (REST) has gained widespread acceptance across the Web as a simpler alternative to SOAP- and Web Services Description Language (WSDL)-based Web services. Key evidence of this shift in interface design is the adoption of REST by mainstream Web 2.0 service providers -- including Yahoo, Google, and Facebook -- who have deprecated or passed on SOAP and WSDL-based interfaces in favor of an easier-to-use, resource-oriented model to expose their services. In this article, Alex Rodriguez introduces you to the basic principles of REST.
This dissertation introduces and elaborates the Representational State Transfer (REST) architectural style for distributed hypermedia systems, describing the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, while contrasting them to the constraints of other architectural styles.
The OpenAPI Initiative (OAI) was created by a consortium of forward-looking industry experts who recognize the immense value of standardizing on how REST APIs are described. As an open governance structure under the Linux Foundation, the OAI is focused on creating, evolving and promoting a vendor neutral description format.
J. Wu, K. Williams, H. Chen, M. Khabsa, C. Caragea, A. Ororbia, D. Jordan, and C. Giles. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence and the Twenty-Sixth Innovative Applications of Artificial Intelligence Conference, Québec, Canada, page 2930--2937. Association for the Advancement of Artificial Intelligence, (July 2014)
M. Strohbach, A. Wiesmaier, and A. Mittelbach. Big Stream Processing Systems (Dagstuhl Seminar 17441), volume 7 of Dagstuhl Seminar, chapter Overview of Talks, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, (2018)