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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.10635 |
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| _version_ | 1866912031056068608 |
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| author | Wang, Yuqing Fariha, Anna |
| author_facet | Wang, Yuqing Fariha, Anna |
| contents | CoWrangler is a data-wrangling recommender system designed to streamline data processing tasks. Recognizing that data processing is often time-consuming and complex for novice users, we aim to simplify the decision-making process regarding the most effective subsequent data operation. By analyzing over 10,000 Kaggle notebooks spanning approximately 1,000 datasets, we derive insights into common data processing strategies employed by users across various tasks. This analysis helps us understand how dataset quality influences wrangling operations, informing our ongoing efforts to possibly expand our dataset sources in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10635 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Development of Data Evaluation Benchmark for Data Wrangling Recommendation System Wang, Yuqing Fariha, Anna Databases CoWrangler is a data-wrangling recommender system designed to streamline data processing tasks. Recognizing that data processing is often time-consuming and complex for novice users, we aim to simplify the decision-making process regarding the most effective subsequent data operation. By analyzing over 10,000 Kaggle notebooks spanning approximately 1,000 datasets, we derive insights into common data processing strategies employed by users across various tasks. This analysis helps us understand how dataset quality influences wrangling operations, informing our ongoing efforts to possibly expand our dataset sources in the future. |
| title | Development of Data Evaluation Benchmark for Data Wrangling Recommendation System |
| topic | Databases |
| url | https://arxiv.org/abs/2409.10635 |