PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, February, 2020
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| Format: | Abschlussarbeit |
| Sprache: | eng |
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Massachusetts Institute of Technology
2021
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| Online Zugang: | https://hdl.handle.net/1721.1/130607 |
| _version_ | 1826197366550560768 |
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| author | Lew, Alexander K. |
| author2 | Vikash K. Mansinghka. |
| author_facet | Vikash K. Mansinghka. Lew, Alexander K. |
| author_sort | Lew, Alexander K. |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, February, 2020 |
| first_indexed | 2024-09-23T10:46:35Z |
| format | Thesis |
| id | mit-1721.1/130607 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:46:35Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1306072021-05-15T03:25:11Z PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming Bayesian data cleaning at scale with domain-specific probabilistic programming Lew, Alexander K. Vikash K. Mansinghka. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, February, 2020 Cataloged from the official PDF version of thesis. Includes bibliographical references (pages 89-93). Data cleaning is naturally framed as probabilistic inference in a generative model, combining a prior distribution over ground-truth databases with a likelihood that models the noisy channel by which the data are filtered, corrupted, and joined to yield incomplete, dirty, and denormalized datasets. Based on this view, this thesis presents PClean, a unified generative modeling architecture for cleaning and normalizing dirty data in diverse domains. Given an unclean dataset and a probabilistic program encoding relevant domain knowledge, PClean learns a structured representation of the data as a relational database of interrelated objects, and uses this latent structure to impute missing values, identify duplicates, detect errors, and propose corrections in the original data table. PClean makes three modeling and inference contributions: (i) a domain-general non-parametric generative model of relational data, for inferring latent objects and their network of latent connections; (ii) a domain-specific probabilistic programming language, for encoding domain knowledge specific to each dataset being cleaned; and (iii) a domain-general inference engine that adapts to each PClean program by constructing data-driven proposals used in sequential Monte Carlo and particle Gibbs. This thesis shows empirically that short (< 50-line) PClean programs deliver higher accuracy than state-of-the-art data cleaning systems based on machine learning and weighted logic; that PClean's inference algorithm is faster than generic particle Gibbs inference for probabilistic programs; and that PClean scales to large real-world datasets with millions of rows. by Alexander K. Lew. S.M. S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2021-05-14T16:29:06Z 2021-05-14T16:29:06Z 2020 2020 Thesis https://hdl.handle.net/1721.1/130607 1249693675 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 93 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Lew, Alexander K. PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming |
| title | PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming |
| title_full | PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming |
| title_fullStr | PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming |
| title_full_unstemmed | PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming |
| title_short | PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming |
| title_sort | pclean bayesian data cleaning at scale with domain specific probabilistic programming |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/130607 |
| work_keys_str_mv | AT lewalexanderk pcleanbayesiandatacleaningatscalewithdomainspecificprobabilisticprogramming AT lewalexanderk bayesiandatacleaningatscalewithdomainspecificprobabilisticprogramming |