Arachnid: Generalized Visual Data Cleaning

Summary: Arachnid generalizes visual data cleaning by translating mouse interactions on common visualizations into a broad set of cleaning transformations. Based on generalized selection and direct manipulation, it enables exploration-driven, on-the-fly cleaning beyond fixed pipelines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5640
Venue: SIGMOD
Year: 2019
Pagerank: 4.3300131e-05
Overall Rank: 9,501 | 33.97%
DOI: 10.1145/3299869.3300108

Incoming Non-self Citations Over Time

Authors

1. Conder L. Shou
2. Amita Shukla

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,983	Towards Scalable Visual Data Wrangling via Direct Manipulation	2026	CIDR	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
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198	Declarative Data Cleaning: Language, Model, and Algorithms	2001	VLDB	0.0003505869
6,383	A Demonstration of DBWipes: Clean as You Query	2012	VLDB	5.0831604e-05
690	AJAX: An Extensible Data Cleaning Tool	2000	SIGMOD	0.00018068707
5,930	ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning	2016	SIGMOD	5.2632185e-05
7,565	PIClean: A Probabilistic and Interactive Data Cleaning System	2019	SIGMOD	4.7048523e-05
8,590	Wisteria: Nurturing Scalable Data Cleaning Infrastructure	2015	VLDB	4.4851741e-05
9,983	Towards Scalable Visual Data Wrangling via Direct Manipulation	2026	CIDR	4.1905499e-05
9,224	VisClean: Interactive Cleaning for Progressive Visualization	2020	VLDB	4.3657563e-05