Using the data warehouse
The impact of poor data quality on the typical enterprise
Communications of the ACM
A multidimensional and multiversion structure for OLAP applications
Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
The EVE Approach: View Synchronization in Dynamic Distributed Environments
IEEE Transactions on Knowledge and Data Engineering
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
Goal-Oriented Requirements Engineering: A Guided Tour
RE '01 Proceedings of the Fifth IEEE International Symposium on Requirements Engineering
DWARF: AN Approach for Requirements Definition and Management of Data Warehouse Systems
RE '03 Proceedings of the 11th IEEE International Conference on Requirements Engineering
Optimizing ETL Processes in Data Warehouses
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Goal-oriented requirement analysis for data warehouse design
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
A Compensation-Based Approach for View Maintenance in Distributed Environments
IEEE Transactions on Knowledge and Data Engineering
Eliminating Duplicates in Information Integration: An Adaptive, Extensible Framework
IEEE Intelligent Systems
Research in data warehouse modeling and design: dead or alive?
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
A Model and Language for Bitemporal Schema Versioning in Data Warehouses
CIC '06 Proceedings of the 15th International Conference on Computing
A Model-based Object-oriented Approach to Requirement Engineering (MORE)
COMPSAC '07 Proceedings of the 31st Annual International Computer Software and Applications Conference - Volume 01
Secure knowledge management: confidentiality, trust, and privacy
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Data warehouse integrate information from numerous data sources under a unified schema and format to provide effective results from multidimensional data analysis in order to facilitate reporting and trend analysis. These information sources are dynamic in nature and keep on changing owing to the autonomous nature of transactions being carried out in the organization along with the complexity involved in gathering requirements from the users. Requirements elicitation and collection is difficult to perform because user needs keep on changing. As a consequence, the data warehouse must evolve so that it improves the data quality by easily incorporating the changes in requirements as well as source schema. In this paper we present a theoretical framework called DWEVOLVE to support data warehouse evolution. The proposed framework enhances the functionality of previously designed framework by taking into account the requirements specified by the users. Provisions have also been made to define and generate customized reports according to the user needs.