2026-09-20T00:27:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1445502022-08-30T03:34:30Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Cafarella, Michael
Becker, Scott
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-08-29T15:55:14Z
2022-08-29T15:55:14Z
2022-05
2022-05-27T16:19:36.858Z
https://hdl.handle.net/1721.1/144550
As the amount of data in the world continues to increase, the ability to track data provenance becomes more and more important. In order to tackle this issue, a tool, Knowledge Network Programming System (KNPS), was built to track data history. To do this, KNPS monitors a computer’s processes and file system in order to determine the events that modified the data on the computer. This paper looks into how the amount of CPU Usage the KNPS tools uses effects how well the tool is able to capture these events. Additionally, a prediction mechanism was built in the tool to try to predict events that were not captured. This effectiveness of the prediction mechanism is explored in this paper as well.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Analyzing a Computer’s Ability to Monitor Data Provenance Events
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>As the amount of data in the world continues to increase, the ability to track data provenance becomes more and more important. In order to tackle this issue, a tool, Knowledge Network Programming System (KNPS), was built to track data history. To do this, KNPS monitors a computer’s processes and file system in order to determine the events that modified the data on the computer. This paper looks into how the amount of CPU Usage the KNPS tools uses effects how well the tool is able to capture these events. Additionally, a prediction mechanism was built in the tool to try to predict events that were not captured. This effectiveness of the prediction mechanism is explored in this paper as well.</Abstract>
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