A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
Federated database systems for managing distributed, heterogeneous, and autonomous databases
ACM Computing Surveys (CSUR) - Special issue on heterogeneous databases
ACM Computing Surveys (CSUR)
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
ICSE '94 Proceedings of the 16th international conference on Software engineering
A product perspective on total data quality management
Communications of the ACM
Laws of Software Evolution Revisited
EWSPT '96 Proceedings of the 5th European Workshop on Software Process Technology
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Pay-as-you-go user feedback for dataspace systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Bootstrapping pay-as-you-go data integration systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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The trend to merge medical practices into cooperatively operating networks and organizational units like Medical Supply Centers generates new challenges for an adequate IT support. In particular, new use cases for common economic planning, controlling and treatment coordination arise. This requires consolidation of data originating from heterogeneous and autonomous software systems. Heterogeneity and autonomy are core reasons for low data quality. The intuitive approach of initially integrating heterogeneous systems into a federated system creates a very high upfront effort before the system can become operable and does not adequately consider the fact that data quality requirements might change over time. To remedy this, we propose an approach for continuous data quality improvement which enables a demand driven step by step system integration. By adapting the generic Total Data Quality Management process to healthcare specific use cases, we are developing an extended model for continuous data quality management in cooperative healthcare settings. The IT tools which are needed to provide the information that drives this process are currently in development within a government supported project involving both industry and academia.