Wrangler: interactive visual specification of data transformation scripts

By Sean Kandel, Andreas Paepcke, Joseph M Hellerstein, Jeffrey Heer. 2011

763 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Human-Computer Interaction, Signal Processing, Software Engineering

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Citations in the corpus

AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, background): “Wrangler [27] and Foofah [26] are text extraction and transformation systems that similarly formulate their problems as search over a language of text transformations, and develop manual pruning heuristics to reduce the search space.”
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Improve Data Data Cleaning [158], [159], [160], [161], [162], [163], [164], [165], [166] Re-labeling [119]”
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-inthe-loop processing with crowds [15,21,22,36,40,52].”
Errudite: Scalable, Reproducible, and Testable Error Analysis (2019, methodology): “It has been widely applied to tasks like data wrangling (Kandel et al., 2011) and text editing (Lau et al., 2003).”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, background): “As a result, large-scale data cleaning has re-emerged as the key goal of many academic [8, 22, 24] and industrial efforts (including Tamr [38], Trifacta Wrangler [25], and many more).”
Collaborative Data Analytics with DataHub (2015, methodology): “Some of the apps in the app ecosystem are inspired by related work: DataQ is similar to visual query builders such as Dataplay [2], but opts for a simpler, more direct specification of SQL commands; Distill is similar to wrangler and flash-fill [9, 8], but gets direct examples from users rather than”
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, background): “Interactive data cleaning focuses on making this process easier, because data transformations can be difficult to specify and reuse across multiple tasks [61, 72, 102].”
Towards Automated Data Cleaning Workflows (2019, background): “Nevertheless, in real-world applications, human agents utilize handcrafted scripts to curate their datasets [28, 11].”
Ground: A Data Context Service (2017, background): “Schema extraction is one important example, in a spectrum from automated techniques [4, 1] to humanguided metadata wrangling [16, 18].”
How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation (2019, background): “In addition to the human users implied by Mentis and by Taylor, Kandel et al. wrote about making data “palatable to databases, statistics packages, and visualization tools” [49].”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, methodology): “Data Wrangler reactively suggests a list of possible transformations from user selection in a visualized interface [74], and also proactively suggests data transformations which maps input data to a relational format expected by analysis tools [64].”
Natural Language to Code Generation in Interactive Data Science Notebooks (2022, background): “, 2021a,b), with particular interest in data wrangling and EDA tasks (Bavishi et al., 2019; Jain et al., 2021; Nazabal et al., 2020; Kandel et al., 2011).”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, methodology): “This list can be extended with Cytoscape [19], Tableau [20], Data Wrangler [21] and others.”
The Data Civilizer System (2017, methodology): “For example, Data Wrangler [16] and DataXFormer [6] automate some aspects of cleaning and transforming data, Data Tamer [21] mainly performs schema mappings and record linkage, NADEEF [10] and BigDansing [17] provide generic programming interface for users to deﬁne their data quality rules, and…”
Spreadsheet table transformations from examples (2011, methodology): “For relative functions RelFunc1 and RelFunc2, ProgFromEx builds the corresponding associative program AssocProg (line [13]).”
How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, methodology): “ of feature-extraction and feature-engineering. This stage is an important site for the design of data [23]. The meaning and nature of the data may be changed [23, 67] during this time-consuming step [34, 48, 67, 80, 103]. During this phase, the use of synchronous discussion tools dropped to nearly ”