Towards reliable interactive data cleaning: a user survey and recommendations
By Sanjay Krishnan, Daniel Haas, M. Franklin, Eugene Wu. 2016
72 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, 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
- Sanjay Krishnan
- Eugene Wu
- M. Franklin
- Ken Goldberg
- Jiannan Wang
- Yinjun Wu
- Susan B. Davidson
- Val Tannen
In Information Systems
- Mohammad Mahdavi
- Larysa Visengeriyeva
- Felix Neutatz
- M. Franklin
- Ken Goldberg
- Jiannan Wang
- Ziawasch Abedjan
- Nithya Sambasivan
In Management Science and Operations Research
- Sanjay Krishnan
- Eugene Wu
- M. Franklin
- Jiannan Wang
- Ken Goldberg
- Mohammad Mahdavi
- Larysa Visengeriyeva
- Felix Neutatz
Citations in the corpus
- AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, background): “As a consequence, the burden on the analyst is gradually shifting away from the design of hand-written data cleaning scripts to building and tuning pipelines of automated data cleaning libraries [29].”
- BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “This company’s data cleaning challenges are not unique and are prevalent in many industrial ML pipelines [31].”
- ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “If one only cleans two of the data points, the intermediate result reveals a misleading trend (Figure 1b).”
- Towards Automated Data Cleaning Workflows (2019, methodology): “We consider a data science use case where data analytics and data preparation are carried out on a frequent basis, accumulating a history of data cleaning tasks from the past that can be logged for later analysis [13].”
- “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, background): “Machine Learning workflows are fundamentally iterative and exploratory in nature [7, 52, 71, 96].”
- PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models (2020, background): “s typically an iterative and interactive process, allowing data analysts to alternate between analysis and cleaning tasks, as well as to interact with other parties such as IT staff and data curators [33]. Machine learning techniques are particularly sensitive to dirty data in training datasets, sin”