Service Workflow Activity Input/Output Parameters Recommendation Method by Combining Transformer and Weighted HITS

Abstract

Each activity in the service workflow interacts with services as required to meet complex business needs and quickly adapt to market changes. The design of each activity's input/output interface parameters influences whether it can successfully map to appropriate and interactive services. In practice, suitable activity interface parameters should possess 3-features: $realism$ , $relevance$ , and $compatibility$ , as popular parameters originating from the real world and closely related to activity semantics are apt to match user-expected services. However, existing research requires expert specification or ontology-based inference, resulting in outdated, inconsistent parameters that lack necessary elements, making it challenging to match expected services. Therefore, we propose an automated method combining Transformer and weighted HITS to recommend interface parameters with 3-features on activity function requirement. It filters similar Endpoints (EPs) based on the activity's semantics by supervised Transformer-based learning of multi-domain APIs and unsupervised EPs matching. Next, a node-weighted heterogeneous graph is built based on similar EPs and their interface parameter relationships. We then apply a node-weighted HITS to explore mutual gain relationships within the graph and calculate parameter compatibilities. Finally, a top- $k$ non-redundant compatible parameter list and corresponding different formats are recommended for the activity. The method's effectiveness and efficiency are verified using a real API service dataset from RapidAPI.