Provenance Mining based Recommendation for Scientific Workflow Composition

In today's web landscape, the vast number of available reusable and universally accessible web services, or so-called Application Programming Interfaces (APIs), facilitates the creation of big data analytics procedures (scientific workflows or workflows in short, or mashups). However, significant challenges are also posed due to the overwhelming variety of options of service candidates to choose from. A manual service selection process is often time-consuming and prone to errors, making it difficult to align service choices with user intentions efficiently. My research addresses this challenge by leveraging machine learning techniques to enhance the accuracy, speed, and robustness of service recommendations. My major contributions are three-fold. First, a novel knowledge graph framework called "Unit of Work" (UoW) knowledge graph (KG) is established, which records service dependencies within and among workflows. Second, service features are treated as first-class entities, transforming low-order UoW KGs into higher-order modal KGs. Third, based on the UoW networks, a generative adversarial network (GAN)-powered "recommend-as-you-go" approach is developed capable of predicting user intent and suggesting the next suitable services during a workflow construction process. Extensive experiments over real-world datasets, such as ProgrammableWeb.com and MyExperiment.org, have demonstrated the effectiveness and efficiency of our techniques. My techniques have also been integrated into the NASA EcoPro project.