Tutorial on
The Web API Lifecycle: Requirements Analysis, Testing, and Application Integration — A Case Study of AI Services APIs.
Instructor
|
Martin Zimmermann
University of Applied Sciences and Arts Lucerne
Switzerland
|
|
|
|
Short Bio
Martin Zimmermann served as the Vice Director of the newly established Department of Computer Science at the Lucerne University of Applied Sciences and Arts. From 1994 to 1998, he worked as a researcher at IBM European Networking Center and Deutsche Bank Group. Between 1998 and 2001, he held a professorship at the University of Applied Sciences Rapperswil in Switzerland. Zimmermann obtained an M.S. (Diplom) from the Karlsruhe Institute of Technology (KIT) and a Ph.D. from J.W. Goethe University Frankfurt in 1996. He has authored over 50 publications, including contributions to international book chapters, journals, and conference proceedings. His work has earned him several best paper awards, particularly for his contributions to visual programming and context-based mobile applications. His research interests encompass mobile devices, the development of cross-platform mobile applications, and visual programming, with a particular emphasis on user-centered, context-based solutions. A significant portion of his work focuses on enhancing the understanding, design, development, and performance of context-aware applications based on Web APIs.
|
Abstract
Web Application Programming Interfaces (Web APIs) are the building blocks that enable rapid assembly of data and functionality across applications by eliminating the necessity to redevelop common functionalities from the ground up. The AI era has amplified their importance, providing access to text generation, speech and vision processing, etc. through scalable APIs.
This tutorial offers a concise, hands-on exploration of Web APIs with a dedicated focus on AI services. Attendees will learn how to discover providers, evaluate capabilities and costs, choose the right API for a given use case, and implement robust integration patterns. A practical case study will demonstrate end-to-end Web AI API selection and integration in both traditional web and mobile (including low-code) contexts. The session combines short theory segments with hands-on activities and a capstone prototype that demonstrates end-to-end API selection, integration, and monitoring.
Keywords
Web APIs, AI services, API Marketplace, Web API evaluation, Web API integration
Aims and Learning Objectives
- Map the Web API landscape: Generalist marketplaces vs. specialized providers, with a dedicated lens on AI services (e.g., NLP, generation, and speech).
- Learn evaluation criteria for Web APIs, including capabilities, latency, reliability, pricing, and data privacy considerations.
- Create a structured Web API selection workflow, and compare multiple providers for the same task (e.g., sentiment analysis, image captioning).
- Implement a practical AI-enabled prototype using multiple AI APIs, leveraging both traditional web apps and low-code environments.
This tutorial will provide an in-depth examination of Web API provides, with a dedicated lens on AI services. Additionally, participants will learn best practices for developing applications using Web APIs, including practical tips for building effective applications.
Target Audience
Researchers, practitioners
Prerequisite Knowledge of Audience
Basic programming experience (preferably in Python or JavaScript) and familiarity with REST/HTTP.
Optional: Basic exposure to cloud services, and Web API testing tools.
Detailed Outline
The tutorial is divided into four parts and will cover the following topics:
Part 1: Exploring Web API Providers Through Concrete Examples (AI services focus):
We will examine various Web API providers and specific examples of their APIs to gain a deeper understanding of their functionalities and typical use cases. Representative examples: a mix of marketplace APIs and specialized AI endpoints (e.g., chat/generation, sentiment analysis, image analysis, audio transcription).
Part 2: Evaluation and Selection of Web API Providers and AI APIs:
We will explore methods and criteria for assessing Web API providers and their APIs, developing tailored recommendations for selecting the most appropriate Web APIs for specific scenarios. We will focus on JSON (JavaScript Object Notation), the most widely used data format for Web APIs, with an emphasis on visualizing JSON data effectively.
Part 3: Integrating Web APIs into Applications :
We will explore best practices for effectively integrating Web APIs into applications, covering essential topics such as planning, implementation, design patterns, and testing strategies.
Part 4: Apply concepts in a realistic AI-focused case study (Practical Exercises):
In the practical part, we will collaboratively build a minimal prototype that combines multiple AI APIs , including both simple web applications and mobile applications developed using low-code environments.