SampleClean: Fast and Reliable Analytics on Dirty Data
By Sanjay Krishnan, Jiannan Wang, M. Franklin, Ken Goldberg, Tim Kraska, Tova Milo, Eugene Wu. 2015
61 citations recorded by the source corpus.
Subfields: Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Evaluation & Benchmarks
- Sanjay Krishnan
- Eugene Wu
- Marc-André Zöller
- Marco F. Huber
- Jiannan Wang
- Ken Goldberg
- M. Franklin
- Ihab F. Ilyas
Citations in the corpus
- Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Yet, most current approaches still aim to assist a human data scientist instead of fully automated data cleaning, (e.g., Krishnan et al., 2015; Khayyaty et al., 2015; Krishnan et al., 2016; Eduardo & Sutton, 2016; Rekatsinas et al., 2017).”
- AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, background): “cleaning [4, 23, 28, 30, 41, 45] has revealed human-time — Machine Learning in Data Cleaning: Machine learning Application-Aware Cleaning: Semantics about the down-stream Generating Cleaning Programs: A composable data”
- Survey on Automated Machine Learning (2019, methodology): “ientist in data cleaning (Galhardas et al., 2000; Raman and Hellerstein, 2001). Yet, most current approaches still aim to assist a human data scientist instead of fully automate data cleaning, e.g., (Krishnan et al., 2015; Khayyaty et al., 2015; Krishnan et al., 2016; Eduardo and Sutton, 2016; Rekat”
- 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, methodology): “The materials for this section are inspired by our work on the SampleClean project [51].”
- Towards reliable interactive data cleaning: a user survey and recommendations (2016, background): “…of aspects of this problem, including reducing the latency of human operations [20], understanding quality in macrotasks [18], reducing latency with sampling [29], mitigating cognitive biases [27], addressing privacy concerns [30], and defining statistical correctness for data cleaning [28] [31].”