Diversified and Compatible Web APIs Recommendation in IoT
release_vjm74boyr5gxhm763rb5rk224u
by
Wenwen Gong, Huiping Wu, Xiaokang Wang, Xuyun Zhang, Yawei Wang, Yifei Chen, Mohammad R. Khosravi
2021
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 web APIs
and further leads to a mashup development failure. Second, existing approaches
often return a web API composition solution to the mashup developer for
reference, which narrows the developer's API selection scope considerably and
may reduce developer satisfaction heavily. In view of the above challenges and
successful application of game theory in the IoT, based on the idea of game
theory, we propose a compatible and diverse web APIs recommendation approach
for mashup creations, named MCCOMP+DIV, to return multiple sets of diverse and
compatible web APIs with higher success rate. Finally, we validate the
effectiveness and efficiency of MCCOMP+DIV through a set of experiments based
on a real-world web API dataset, i.e., the PW dataset crawled from
ProgrammableWeb.com.
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