CURED: Creating, Understanding, and Repairing Errors Demonstrator

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Nicholas Chandler, Sebastian Jäger, Philipp Jung, Felix Bießmann
• Venue: arXiv: cs.LG
• Published: 2026-07-22
• Collected: 2026-07-23
• URL: http://arxiv.org/abs/2607.20140v1
• PDF: https://arxiv.org/pdf/2607.20140v1
• DOI: UNKNOWN

Plain-Language Brief

The paper presents CURED, a web application that allows users to upload tabular data, introduce errors, and use machine learning methods to clean and understand the error mechanisms in the data. The goal is to bridge the gap between theoretical advancements and practical insights in error models and data cleaning algorithms for tabular data.

What the Document Says

• The paper discusses the importance of error detection and cleaning in data-intensive software applications, particularly in the context of tabular data.
• It mentions that statistical learning algorithms have potential for error detection and cleaning at the intersection of Machine Learning (ML) and Database Management Systems (DBMS).
• The authors combine their recent work on ML-based data cleaning and error models into a unified demonstrator called CURED.
• CURED is a web application that allows users to upload tabular data, perturb the data with realistic errors, and use modern ML methods for cleaning and understanding error mechanisms.
• The application aims to bridge the gap between theoretical advancements and practical insights in the context of error models and data cleaning algorithms for tabular data.
• The CURED demonstrator is available at https://cured.demo.calgo-lab.de/

Key Concepts Mentioned

• Machine Learning
• Data Cleaning
• Error Detection
• Tabular Data
• CURED Demonstrator

Claims Made by the Document

• Statistical learning algorithms have potential for error detection and cleaning at the intersection of ML and DBMS.
• Combining ML-based data cleaning and error models can create a unified demonstrator that bridges the gap between theoretical advancements and practical insights.

Evidence Used

• The text discusses the potential of statistical learning algorithms for error detection and cleaning.
• The authors combine their recent work on ML-based data cleaning and error models in a unified demonstrator.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications. Recent research at the intersection of Machine Learning (ML) and Database Management Systems (DBMS) highlights the potential of statistical learning algorithms for error detection and cleaning. This paper combines our recent work on ML-based data cleaning and error models in a unified demonstrator. The web application allows users to upload tabular data, perturb the data with realistic data dependent errors and use modern ML methods to clean and understand error mechanisms in data. Our demonstrator helps to bridge the gap between theoretical advancements and intuitive practical insights in the context of error models and data cleaning algorithms for tabular data. The demonstrator is available at https://cured.demo.calgo-lab.de/