Abstract

Scientific workflows define computational processes needed for carrying out scientific experiments. Existing workflow repositories contain hundreds of scientific workflows, where scientists can find materials and knowledge to facilitate workflow design for running related experiments. Identifying reusable fragments in growing workflow repositories has become increasingly important. In this paper, we present PSM-Flow, a probabilistic subgraph mining algorithm designed to discover commonly occurring fragments in a workflow corpus using a modified version of the Latent Dirichlet Allocation algorithm. The proposed model encodes the geodesic distance between workflow steps into the model for implicitly modeling fragments. PSM-Flow captures variations of frequent fragments while maintaining its space complexity bounded polynomially, as it requires no candidate generation. We applied PSM-Flow to three real …

Date

December 3, 2018

Authors

Chin Wang Cheong, Daniel Garijo, Cheung Kwok Wai, Yolanda Gil

Conference

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

Pages

166-173

Publisher

IEEE