2026-09-19T17:15:08Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1431612022-06-16T03:06:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Veeramachaneni, Kalyan
Xie, Zhuofan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-06-15T13:00:31Z
2022-06-15T13:00:31Z
2022-02
2022-02-22T18:32:25.576Z
https://hdl.handle.net/1721.1/143161
In databases, many data do not come from scratch. They are derived from some other data and what describes this is called data lineage. Knowing the data lineage could help us do data validation, error detection, data debugging, and privacy and access control. Unfortunately, many databases do not have well documented data lineage information, and most existing works in this area heavily relies on extra input such as metadata, source code or annotations. In this paper, we build upon Tracer, a previously purposed machine learning approach to this problem, and make it more accurate, more general, and more intuitive.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Tracer: A Machine Learning Based Data Lineage Solver with Visualized Metadata Management
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>In databases, many data do not come from scratch. They are derived from some other data and what describes this is called data lineage. Knowing the data lineage could help us do data validation, error detection, data debugging, and privacy and access control. Unfortunately, many databases do not have well documented data lineage information, and most existing works in this area heavily relies on extra input such as metadata, source code or annotations. In this paper, we build upon Tracer, a previously purposed machine learning approach to this problem, and make it more accurate, more general, and more intuitive.</Abstract>
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