Consistent, accurate and timely data are essential to the functioning of a modern organization. Managing the integrity of an organization’s data assets in a systematic manner is a challenging task in the face of continuous update, transformation and processing to support business operations. Classic approaches to constraint-based integrity focus on logical consistency within a database and reject any transaction that violates consistency, but leave unresolved how to fix or manage violations. More ad hoc approaches focus on the accuracy of the data and attempt to clean data assets after the fact, using queries to flag records with potential violations and using manual efforts to repair. Neither approach satisfactorily addresses the problem from an organizational point of view. In this thesis, we provide a conceptual model of constraint-based integrity management (CBIM) that flexibly combines both approaches in a systematic manner to provide improved integrity management. We perform a gap analysis that examines the criteria that are desirable for efficient management of data integrity. Our approach involves creating a Data Integrity Zone and an On Deck Zone in the database for separating the clean data from data that violates integrity constraints. We provide tool support for specifying constraints in a tabular form and generating triggers that flag violations of dependencies. We validate this by performing case studies on two systems used to manage healthcare data: PAL-IS and iMED-Learn. Our case studies show that using views to implement the zones does not cause any significant increase in the running time of a process.

Consistent, accurate and timely data are essential to the functioning of a modern organization. Managing the integrity of an organization’s data assets in a systematic manner is a challenging task in the face of continuous update, transformation and processing to support business operations. Classic approaches to constraint-based integrity focus on logical consistency within a database and reject any transaction that violates consistency, but leave unresolved how to fix or manage violations. More ad hoc approaches focus on the accuracy of the data and attempt to clean data assets after the fact, using queries to flag records with potential violations and using manual efforts to repair. Neither approach satisfactorily addresses the problem from an organizational point of view. In this thesis, we provide a conceptual model of constraint-based integrity management (CBIM) that flexibly combines both approaches in a systematic manner to provide improved integrity management. We perform a gap analysis that examines the criteria that are desirable for efficient management of data integrity. Our approach involves creating a Data Integrity Zone and an On Deck Zone in the database for separating the clean data from data that violates integrity constraints. We provide tool support for specifying constraints in a tabular form and generating triggers that flag violations of dependencies. We validate this by performing case studies on two systems used to manage healthcare data: PAL-IS and iMED-Learn. Our case studies show that using views to implement the zones does not cause any significant increase in the running time of a process.

Consistent, accurate and timely data are essential to the functioning of a modern organization. Managing the integrity of an organization’s data assets in a systematic manner is a challenging task in the face of continuous update, transformation and processing to support business operations. Classic approaches to constraint-based integrity focus on logical consistency within a database and reject any transaction that violates consistency, but leave unresolved how to fix or manage violations. More ad hoc approaches focus on the accuracy of the data and attempt to clean data assets after the fact, using queries to flag records with potential violations and using manual efforts to repair. Neither approach satisfactorily addresses the problem from an organizational point of view. In this thesis, we provide a conceptual model of constraint-based integrity management (CBIM) that flexibly combines both approaches in a systematic manner to provide improved integrity management. We perform a gap analysis that examines the criteria that are desirable for efficient management of data integrity. Our approach involves creating a Data Integrity Zone and an On Deck Zone in the database for separating the clean data from data that violates integrity constraints. We provide tool support for specifying constraints in a tabular form and generating triggers that flag violations of dependencies. We validate this by performing case studies on two systems used to manage healthcare data: PAL-IS and iMED-Learn. Our case studies show that using views to implement the zones does not cause any significant increase in the running time of a process.

Consistent, accurate and timely data are essential to the functioning of a modern organization. Managing the integrity of an organization’s data assets in a systematic manner is a challenging task in the face of continuous update, transformation and processing to support business operations. Classic approaches to constraint-based integrity focus on logical consistency within a database and reject any transaction that violates consistency, but leave unresolved how to fix or manage violations. More ad hoc approaches focus on the accuracy of the data and attempt to clean data assets after the fact, using queries to flag records with potential violations and using manual efforts to repair. Neither approach satisfactorily addresses the problem from an organizational point of view. In this thesis, we provide a conceptual model of constraint-based integrity management (CBIM) that flexibly combines both approaches in a systematic manner to provide improved integrity management. We perform a gap analysis that examines the criteria that are desirable for efficient management of data integrity. Our approach involves creating a Data Integrity Zone and an On Deck Zone in the database for separating the clean data from data that violates integrity constraints. We provide tool support for specifying constraints in a tabular form and generating triggers that flag violations of dependencies. We validate this by performing case studies on two systems used to manage healthcare data: PAL-IS and iMED-Learn. Our case studies show that using views to implement the zones does not cause any significant increase in the running time of a process.