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Everything about Data cleansing
Correcting inaccurate computer records
Data cleansing or data cleaning is the process of identifying and correcting corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. Data cleansing can be performed interactively using data wrangling tools, or through batch processing often via scripts or a data quality firewall. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Data cleansingA 5-minute orientation
- Skim a recent paper: Data Cleansing for Salt Dome Dataset With Noise Robust Network on Segmentation TaskInfluential work since 2022
- Pick up a classic book: Smart gridsThe most-reprinted book on the subject
- Read the landmark paper: Real-world Data is Dirty: Data Cleansing and The Merge/Purge ProblemCited 524 times
- Go sideways: Data validationA closely related topic
- Go sideways: Extract, transform, loadA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
A Review on Data Cleansing Methods for Big Data
Automated Load Curve Data Cleansing in Power Systems
Legal aspects of data cleansing in medical AI
Data cleansing mechanisms and approaches for big data analytics: a systematic study
A Bayesian Inference-Based Framework for RFID Data Cleansing
A Model-Based Approach for Developing Data Cleansing Solutions
Planning meets Data Cleansing
🆕 Influential since 2022
Data Cleansing for Salt Dome Dataset With Noise Robust Network on Segmentation Task
Cleansing data from an electronic feeding station to improve estimation of feed efficiency
W2WNet: A two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality
AI-Driven Adaptive Data Cleansing: Automating Error Detection and Correction for Dynamic Datasets
Improving Text Classification of Imbalanced Call Center Conversations Through Data Cleansing, Augmentation, and NER Metadata
📚 Books worth reading
Smart gridsDavid Bakken, Krzysztof Iniewski · 2014
Machine learning with RBrett Lantz · 2013
The SacramentsFrancis J. Hall · 1921
An introduction to duplicate detectionFelix Naumann · 2010
Pressure UlcersBarbara Acello · 2010- 📕Excel Data Cleansing Straight to the PointMrExcel's Holy Macro! Books, Oz du Soleil · 2024
- 📕Excel Data Cleansing Tip Card:(laminated Tip Card)Anne Walsh · 2015
- 📕Problems, methods, and challenges in comprehensive data cleansingHeiko Müller · 2005
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Data validation In computing, data validation or input validation is the process of ensuring data has undergone data cleansing to confirm it has data quality, that is…
- Extract, transform, load Extract, transform, load (ETL) is a three-phase computing process where data are extracted from an input source, transformed (including cleaning), and…
- Data integrity Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle and is a critical aspect to the design…
- Data preparation Data preparation is the act of manipulating (or pre-processing) raw data (which may come from disparate data sources) into a form that can be readily and…
- Surrogate key A surrogate key (or synthetic key, pseudokey, entity identifier, factless key, or technical key[citation needed]) in a database is a unique identifier…
- Data quality Data quality refers to the condition of data based on factors such as accuracy, completeness, consistency, reliability, and whether it is fit for its intended…
- Referential integrity Referential integrity is a property of data stating that all its references are valid. In the context of relational databases, it requires that if a value…
- Foreign key A foreign key is a set of attributes in a table that refers to the primary key of another table, linking these two tables. In the context of relational…