Unsupervised Workflow Discovery in Provenance Graphs
Name
Yue-kevinyue-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
1.35 MB
Format
Adobe PDF
Checksum (MD5)
807fcc647ccc5b0fde10a99f8012eb6e
Author(s)
Yue, Kevin
Advisor(s)
Cafarella, Michael
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Data is an ever-expanding part of life in today’s world. Understanding the origin and the history of data - a concept known as data provenance - can thus be extremely important. In this thesis, we first address the need for a data provenance knowledge graph system, then address the need for being able to recover workflows that exist in such provenance networks, in an unsupervised manner. Along with evaluating the effectiveness of existing unsupervised community and motif detection methods, we also suggest a novel approach that augments standard motif detection. Our research shows weak precision and recall numbers for almost all considered approaches, but provides a promising basis for future experimentation using more multifaceted methods.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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