Wisteria: Nurturing Scalable Data Cleaning Infrastructure

By Daniel Haas, Sanjay Krishnan, Jiannan Wang, M. Franklin, Eugene Wu. 2015

43 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sanjay Krishnan
2. Eugene Wu
3. Ihab F. Ilyas
4. M. Franklin
5. Jiannan Wang
6. Ken Goldberg
7. Xu Chu
8. Tim Kraska

In Computer Science Applications

1. Sanjay Krishnan
2. Tova Milo
3. Eugene Wu
4. Jiannan Wang
5. M. Franklin
6. Xu Chu
7. Ihab F. Ilyas
8. Tim Kraska

In Management Science and Operations Research

1. Sanjay Krishnan
2. Eugene Wu
3. M. Franklin
4. Jiannan Wang
5. Ihab F. Ilyas
6. Ken Goldberg
7. Xu Chu
8. Tova Milo

Citations in the corpus

• AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, background): “These ideas were later extended in the Wisteria project [22] to parameterize the transformations to allow for learning and crowdsourcing.”
• BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “Several tools have been proposed to reduce human burden including automatically generating explanations [50], building more robust interfaces [3,26], and more sophisticated human-in-the-loop processing with crowds [15,21,22,36,40,52].”
• ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning (2016, methodology): “The selection technique applied in ActiveClean uses pointwise gradients to generalize the outlier filtering heuristics to select potentially dirty data even in complex models.”
• Data Cleaning: Overview and Emerging Challenges (2016, background): “…been a surge of interest from both industry and academia on different aspects of data cleaning including new abstractions [10, 30, 77, 22, 33, 14, 73], interfaces [1, 26], approaches for scalability [75, 45, 3, 53, 67], and crowdsourcing techniques [35, 69, 62, 18, 82, 23, 37, 60, 61, 76, 71, 78].”
• Towards reliable interactive data cleaning: a user survey and recommendations (2016, background): “…become easier to acquire and store ever larger datasets, the challenges associated with large-scale data cleaning, wherein issues caused by incorrect, missing, and duplicate data are identified and repaired, has become a subject of intense interest in the academic community e.g., [12] [19] [26].”
• PrivateClean: Data Cleaning and Differential Privacy (2016, background): “We also believe that PrivateClean will be increasingly relevant in human-in-the-loop systems as crowds are increasingly employed in data cleaning [16] and inadvertent data leaks can actually bias the crowds response [22].”
• SampleClean: Fast and Reliable Analytics on Dirty Data (2015, background): “SampleClean [45] and all of its extensions [30, 31, 25], work in the budgeted data cleaning setting.”