Article: Repairing integrity rules for improved data quality Journal: International Journal of Information Quality (IJIQ) 2014 Vol.3 No.4 pp.273 - 297 Abstract: Integrity constraints are the primary tool used to capture business rules and domain constraints in data management systems. When these constraints are not strictly enforced, poor data quality often arises, as inconsistencies occur between the data and the set of constraints. To resolve these inconsistencies, organisations often implement specific, sometimes manual, cleansing routines to fix the errors. As modern systems are expected to handle increasing amounts of highly heterogeneous data, often in dynamic data environments where the data and the constraints may change, manual cleansing routines are insufficient to handle this increased scale and heterogeneity. In this work, we present a set of new constraint repair operations that can be incorporated into a data quality tool that provides automated support for both data and constraint repair and management. Our holistic approach is designed to facilitate the curation and maintenance of both the data and the constraints. We focus on discovering trends, contextual information, and data patterns to understand how a business rule (constraint) has evolved. We also investigate how to find a minimal set of constraints that contain non-redundant information since enforcing extraneous constraints is costly and can negatively affect system performance. We conduct two case studies using real business datasets that demonstrate the quality and usefulness of our techniques. Inderscience Publishers - linking academia, business and industry through research

Title: Repairing integrity rules for improved data quality

Authors: Fei Chiang; Yu Wang

Addresses: Department of Computing and Software, McMaster University, 1280 Main St., Hamilton, Ontario, L8S 4K1, Canada ' Department of Computing and Software, McMaster University, 1280 Main St., Hamilton, Ontario, L8S 4K1, Canada

Abstract: Integrity constraints are the primary tool used to capture business rules and domain constraints in data management systems. When these constraints are not strictly enforced, poor data quality often arises, as inconsistencies occur between the data and the set of constraints. To resolve these inconsistencies, organisations often implement specific, sometimes manual, cleansing routines to fix the errors. As modern systems are expected to handle increasing amounts of highly heterogeneous data, often in dynamic data environments where the data and the constraints may change, manual cleansing routines are insufficient to handle this increased scale and heterogeneity. In this work, we present a set of new constraint repair operations that can be incorporated into a data quality tool that provides automated support for both data and constraint repair and management. Our holistic approach is designed to facilitate the curation and maintenance of both the data and the constraints. We focus on discovering trends, contextual information, and data patterns to understand how a business rule (constraint) has evolved. We also investigate how to find a minimal set of constraints that contain non-redundant information since enforcing extraneous constraints is costly and can negatively affect system performance. We conduct two case studies using real business datasets that demonstrate the quality and usefulness of our techniques.

Keywords: data quality; information quality; data inconsistencies; rule repair; data repair; constraint repair; functional dependencies; integrity rules; data management systems; constraints; business data.

DOI: 10.1504/IJIQ.2014.068653

International Journal of Information Quality, 2014 Vol.3 No.4, pp.273 - 297

Received: 03 Oct 2013
Accepted: 15 Jan 2014
Published online: 17 Apr 2015 *

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Title: Repairing integrity rules for improved data quality

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