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Pattern Functional Dependencies for Data Cleaning Abdelhalim Nathan Nan Tang Utrecht UniversityMourad OuzzaniQatar Computing Research Institutea.a.a.qahtan@uu.nl yang.cao@ed.ac.ukUniversity of Edinburgh
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How to fill out pattern functional dependencies for

01
Identify the set of attributes in the relation.
02
Determine the functional dependencies between these attributes.
03
Start by considering each attribute in the set as a singleton left-hand side for functional dependencies.
04
Combine the attributes to form composite left-hand sides for functional dependencies.
05
Repeat steps 3 and 4 until all possible combinations are covered.
06
Verify if any new functional dependencies violate the transitivity rule.
07
Remove any redundant functional dependencies.
08
Write down the final set of functional dependencies.

Who needs pattern functional dependencies for?

01
Pattern functional dependencies are needed by database designers, architects, and administrators who are responsible for designing and maintaining relational databases.
02
They are crucial for accurately representing the relationships and dependencies between attributes in a database schema.
03
Understanding and documenting functional dependencies help with normalization, query optimization, data integrity, and overall database design quality.
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Pattern functional dependencies are used to identify and analyze the relationships between different variables in a dataset, helping to ensure data integrity and optimize database design.
Entities that manage databases or work with large sets of data, such as organizations and businesses that are subject to data governance policies, are required to file pattern functional dependencies.
To fill out pattern functional dependencies, one must specify the set of attributes involved, describe the dependencies using notation, and provide examples of how these dependencies manifest in the data.
The purpose of pattern functional dependencies is to establish a clear framework for data consistency and validation, which aids in preventing data anomalies and optimizing database operations.
Information that must be reported includes the variables involved, the nature of the dependencies among them, any constraints applied, and examples illustrating these dependencies.
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