SC22 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

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Interactive NLU-Powered Ontology-Based Workflow Synthesis for FAIR Support of HPC

Workshop: HUST-22: 9th International Workshop on HPC User Support Tools

Authors: Zifan Nan, Mithil Dave, and Xipeng Shen (North Carolina State University); Chunhua Liao, Tristan Vanderbruggen, and Pei-Hung Lin (Lawrence Livermore National Laboratory); and Murali Emani (Argonne National Laboratory (ANL))

Abstract: Workflow synthesis is important for automatically creating the data processing workflow in a FAIR data management system for HPC. Previous methods are table-based, rigid and not scalable. This paper addresses these limitations by developing a new approach to workflow synthesis, interactive NLU-powered ontology-based workflow synthesis (INPOWS). INPOWS allows the use of Natural Language for queries, maximizes the robustness in handling concepts and language ambiguities through an interactive ontology-based design, and achieves superior extensibility by adopting a synthesis algorithm powered by Natural Language Understanding. In our experiments, INPOWS shows the efficacy in enabling flexible, robust, and extensible workflow synthesis.


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