Diversified and Compatible Web APIs Recommendation in IoT
Wenwen Gong, Huiping Wu, Xiaokang Wang, Xuyun Zhang, Yawei Wang, Yifei, Chen, Mohammad R. Khosravi

TL;DR
This paper introduces MCCOMP+DIV, a game theory-based approach for recommending diverse and compatible web APIs in IoT mashup development, addressing compatibility and diversity issues to improve success rates.
Contribution
It presents a novel game theory-inspired method that ensures compatibility and diversity in web API recommendations for IoT mashups, enhancing developer satisfaction.
Findings
Higher success rate in API mashup creation
Effective handling of API compatibility issues
Improved diversity in recommended API sets
Abstract
With the ever-increasing popularity of Service-oriented Architecture (SoA) and Internet of Things (IoT), a considerable number of enterprises or organizations are attempting to encapsulate their provided complex business services into various lightweight and accessible web APIs (application programming interfaces) with diverse functions. In this situation, a software developer can select a group of preferred web APIs from a massive number of candidates to create a complex mashup economically and quickly based on the keywords typed by the developer. However, traditional keyword-based web API search approaches often suffer from the following difficulties and challenges. First, they often focus more on the functional matching between the candidate web APIs and the mashup to be developed while neglecting the compatibility among different APIs, which probably returns a group of incompatible…
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Taxonomy
TopicsService-Oriented Architecture and Web Services · Caching and Content Delivery · Cloud Computing and Resource Management
