Causal Analysis Experiments on Log Extraction and Processing for Causal Insights
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 d...
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| Other Authors: | |
| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/151420 |
| _version_ | 1811088218837221376 |
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| author | Khine, Min Thet |
| author2 | Cafarella, Michael |
| author_facet | Cafarella, Michael Khine, Min Thet |
| author_sort | Khine, Min Thet |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:58:34Z |
| format | Thesis |
| id | mit-1721.1/151420 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:58:34Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1514202023-08-01T03:29:10Z Causal Analysis Experiments on Log Extraction and Processing for Causal Insights Khine, Min Thet Cafarella, Michael Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. 2023-07-31T19:38:27Z 2023-07-31T19:38:27Z 2023-06 2023-06-06T16:35:16.693Z Thesis https://hdl.handle.net/1721.1/151420 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Khine, Min Thet Causal Analysis Experiments on Log Extraction and Processing for Causal Insights |
| title | Causal Analysis Experiments on Log Extraction and
Processing for Causal Insights |
| title_full | Causal Analysis Experiments on Log Extraction and
Processing for Causal Insights |
| title_fullStr | Causal Analysis Experiments on Log Extraction and
Processing for Causal Insights |
| title_full_unstemmed | Causal Analysis Experiments on Log Extraction and
Processing for Causal Insights |
| title_short | Causal Analysis Experiments on Log Extraction and
Processing for Causal Insights |
| title_sort | causal analysis experiments on log extraction and processing for causal insights |
| url | https://hdl.handle.net/1721.1/151420 |
| work_keys_str_mv | AT khineminthet causalanalysisexperimentsonlogextractionandprocessingforcausalinsights |