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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