Data Curation at Scale: The Data Tamer System

By M. Stonebraker, Daniel Bruckner, Ihab F. Ilyas, George Beskales, Mitch Cherniack, S. Zdonik, Alexander Pagan, Sha Xu. 2013

244 citations recorded by the source corpus.

How researchers see this paper

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

Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Data Tamer [39] is an end-to-end data curation system that can clean and transform datasets and semantically integrate with other datasets.”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, background): “Many data cleaning solutions, including tools for entity resolution [47], expose similar knobs to users.”
Foundations of Crowd Data Sourcing (2015, background): “For example, common tasks to the crowd include classifying single items [6, 19, 23, 24], comparing sets of items [9, 7, 11, 29], and providing missing data items and values [10, 20, 21, 25] in domains where human judgment is required (e.”
Foofah: Transforming Data By Example (2017, background): “Recent research into automated and assisted data transformation systems have tried to reduce the need of a programming background for users, with some success [19, 22, 41].”
Wisteria: Nurturing Scalable Data Cleaning Infrastructure (2015, methodology): “In this paper, we introduce Wisteria, a system designed to support the iterative development and optimization of data cleaning plans end to end.”
Corleone: hands-off crowdsourcing for entity matching (2014, background): “Currently we deem this is the case if A × B exceeds a threshold t B , set to be the largest number such that if after blocking we have t B tuple pairs, then we can ﬁt the feature vectors of all these pairs in memory (we discuss feature vectors below), thus minimizing I/O costs for subsequent steps.”
Can Foundation Models Wrangle Your Data? (2022, methodology): “Additionally, there are statistical-based approaches such as pattern enforcement [24, 45], outlier detection [28], and record deduplication [89] algorithms.”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, background): “Approaches that avoid the minimal repair heuristic generate verified fixes, for example, by using reference master data and editing rules [50], or by heavily involving human experts [126, 105].”
Effective Data Cleaning with Continuous Evaluation (2016, unknown): “Through our experience with deploying Tamr 1 (the commercial descendant of the Data Tamer System [16]) in multiple large enterprises (e.g., an international automotive manufacturer) with large-scale data cleaning projects, it was evident that one of the biggest challenges in adopting a data…”
Toward a System Building Agenda for Data Integration (and Data Science) (2018, background): “The vast majority of DI works (with the exception of efforts such as Tamr and Trifacta [36, 15]) have focused on developing algorithmic solutions.”
Data Integration: The Current Status and the Way Forward (2018, methodology): “The company is based on the Data Tamer academic system [13].”
Data-centric Artificial Intelligence: A Survey (2023, unknown): “Collection Dataset discovery Collaboration Minimum [28, 75, 167] Data integration Automation Programmatic [128, 137] Data integration Automation Learning-based [220, 221] Raw data synthesis Automation Programmatic [133]”
Detecting Data Errors: Where are we and what needs to be done? (2016, methodology): “It is the commercial descendant of the Data Tamer system [30].”
The Billion Prices Project: Using Online Prices for Measurement and Research (2016, methodology): “7 in the spirit of Stonebraker et al. (2013). It involves carefully identifying the retailers that will serve as data sources, using web-scraping software to collect the data, then cleaning, homogenizing, categorizing, and finally extracting the information so it can be used in measurement and resea”
Data Wrangling: The Challenging Yourney from the Wild to the Lake (2015, background): “Many recent efforts have focused on providing automation and tools to enable less skilled workers to clean, integrate and link data [20][26][31], thus enabling the flow of contextual data into an enterprise to be more fluid.”
Towards reliable interactive data cleaning: a user survey and recommendations (2016, background): “While human-in-the-loop data cleaning systems have been extensively studied [10] [12] [17] [19] [33] [37] [41], a key missing piece is a detailed study of how data analysts interact with such systems.”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “Data Tamer [173] uses a crowd of domain experts to resolve uncertain examples in schema”