2026-09-19T23:52:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1514202023-08-01T03:29:10Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Cafarella, Michael
Khine, Min Thet
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:38:27Z
2023-07-31T19:38:27Z
2023-06
2023-06-06T16:35:16.693Z
https://hdl.handle.net/1721.1/151420
Recent decades have seen tremendous advancements in the design and implementation of data processing systems for various applications and use cases. However, even systems that support the most complex queries are mostly used for business reporting, prediction, and classification tasks based on the data. These systems do not necessarily inform users of the causal relationships that are inherent in the data. To this end, we design a new log-based data processing system that provides answers to causal questions based on timestamped logs. This thesis work focuses on improving the current log extraction methods and performing causal analysis experiments on inferred causal models extracted from the logs.
M.Eng.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Causal Analysis Experiments on Log Extraction and
Processing for Causal Insights
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Recent decades have seen tremendous advancements in the design and implementation of data processing systems for various applications and use cases. However, even systems that support the most complex queries are mostly used for business reporting, prediction, and classification tasks based on the data. These systems do not necessarily inform users of the causal relationships that are inherent in the data. To this end, we design a new log-based data processing system that provides answers to causal questions based on timestamped logs. This thesis work focuses on improving the current log extraction methods and performing causal analysis experiments on inferred causal models extracted from the logs.</Abstract>
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