Please use this identifier to cite or link to this item: https://doi.org/10.1145/1989323.1989336
Title: Automatic discovery of attributes in relational databases
Authors: Zhang, M. 
Hadjieleftheriou, M.
Ooi, B.C. 
Procopiuc, C.M.
Srivastava, D.
Keywords: attribute discovery
schema matching
Issue Date: 2011
Source: Zhang, M.,Hadjieleftheriou, M.,Ooi, B.C.,Procopiuc, C.M.,Srivastava, D. (2011). Automatic discovery of attributes in relational databases. Proceedings of the ACM SIGMOD International Conference on Management of Data : 109-120. ScholarBank@NUS Repository. https://doi.org/10.1145/1989323.1989336
Abstract: In this work we design algorithms for clustering relational columns into attributes, i.e., for identifying strong relationships between columns based on the common properties and characteristics of the values they contain. For example, identifying whether a certain set of columns refers to telephone numbers versus social security numbers, or names of customers versus names of nations. Traditional relational database schema languages use very limited primitive data types and simple foreign key constraints to express relationships between columns. Object oriented schema languages allow the definition of custom data types; still, certain relationships between columns might be unknown at design time or they might appear only in a particular database instance. Nevertheless, these relationships are an invaluable tool for schema matching, and generally for better understanding and working with the data. Here, we introduce data oriented solutions (we do not consider solutions that assume the existence of any external knowledge) that use statistical measures to identify strong relationships between the values of a set of columns. Interpreting the database as a graph where nodes correspond to database columns and edges correspond to column relationships, we decompose the graph into connected components and cluster sets of columns into attributes. To test the quality of our solution, we also provide a comprehensive experimental evaluation using real and synthetic datasets. © 2011 ACM.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
URI: http://scholarbank.nus.edu.sg/handle/10635/42024
ISBN: 9781450306614
ISSN: 07308078
DOI: 10.1145/1989323.1989336
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