Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition

• URL: http://arxiv.org/abs/2205.11771v1
• Date: Tue, 24 May 2022 04:18:01 GMT
• Title: Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition
• Authors: Xihao Xie, Jia Zhang, Rahul Ramachandran, Tsengdar J. Lee, Seungwon Lee
• Abstract summary: This paper proposes a novel NLP-inspired approach to recommending services throughout a workflow development process. A workflow composition process is formalized as a step-wise, context-aware service generation procedure. Service embeddings are then learned by applying deep learning model from the NLP field.
• Score: 6.17189383632496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition. This paper proposes a novel NLP-inspired approach to recommending services throughout a workflow development process, based on incrementally learning latent service representation from workflow provenance. A workflow composition process is formalized as a step-wise, context-aware service generation procedure, which is mapped to next-word prediction in a natural language sentence. Historical service dependencies are extracted from workflow provenance to build and enrich a knowledge graph. Each path in the knowledge graph reflects a scenario in a data analytics experiment, which is analogous to a sentence in a conversation. All paths are thus formalized as composable service sequences and are mined, using various patterns, from the established knowledge graph to construct a corpus. Service embeddings are then learned by applying deep learning model from the NLP field. Extensive experiments on the real-world dataset demonstrate the effectiveness and efficiency of the approach.

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