ABSTRACT
Many modern organizations operate in a dynamic and large-scaled web-based environment that involves critical and frequent interactions with external organizations. Within this context, the associated data warehousing environment is often dynamic and evolutionary, with sometimes frequent changes in the quality of various resources (e.g. source systems, processes, metadata), and also in the users' quality requirements. There is thus the need to not only capture quality changes on-the-fly, but also provide automated quality notifications to relevant end users. Managing data warehousing systems in such environment imposes many new challenges for DW design, management and maintenance. In this paper, we propose an extended data warehousing systems architecture that incorporates and extends the concepts of the Quality Factory (QF) and the Quality Notification Service (QNS) that were previously presented in the Cooperative Information Systems (CIS) literature.
- Inmon, W. H. Building the Data Warehouse. John Wiley & Sons, Inc., New York, 2002. Google ScholarDigital Library
- Beyer, M. A. Overview of Data Warehouse Project Delivery in 2009. Gartner Inc., 2009.Google Scholar
- Jarke, M. and Vassiliou, Y. Data Warehouse Quality: A Review of the DWQ Project. In Proceedings of the 2nd Conference on Information Quality (Cambridge, MA, 1997).Google Scholar
- Ballou, D. P. and Tayi, G. K. Enhancing Data Quality in Data Warehouse Environments. Communications of the ACM, 42, 1 (Jan 1999), 73--78. Google ScholarDigital Library
- Rundensteiner, E. A., Koeller, A. and Zhang, X. Maintaining data warehouses over changing information sources. Communications of the ACM, 43, 6 (Jun. 2000), 57--62. Google ScholarDigital Library
- Jarke, M., Jeusfeld, M. A., Quix, C. and Vassiliadis, P. Architecture and quality in data warehouses: An extended repository approach. Information Systems, 24, 3 (May 1999), 229--253.Google ScholarCross Ref
- Lee, Y. W., Strong, D. M., Kahn, B. K. and Wang, R. Y. AIMQ: a methodology for information quality assessment. Information & Management, 40, 2 (Dec. 2002), 133--146. Google ScholarDigital Library
- Scannapieco, M., Virgillito, A., Marchetti, C., Mecella, M. and Baldoni, R. The DaQuinCIS architecture: a platform for exchanging and improving data quality in cooperative information systems. Information Systems, 2004, 7 2003), 551--582. Google ScholarDigital Library
- Scannapieco, M., Virgillito, A., Marchetti, C., Mecella, M. and Baldoni, R. The DaQuinCIS Architecture: a Platform for Exchanging and Improving Data Quality in Cooperative Information Systems. Information Systems, 29, 7 (Oct. 2004), 551--582. Google ScholarDigital Library
- Martin, J. Privacy, Accuracy and Security in Computer Systems. Prentice-Hall, Inc, Englewood Cliffs, NJ, 1974.Google Scholar
- Ballou, D. P. and Pazer, H. L. Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems. Management Science, 31, 2 (Feb. 1985), 150--162.Google Scholar
- Wixom, B. H. and Watson, H. J. An Empirical Investigation of the Factors Affecting Data Warehousing Success. MIS Quarterly, 25, 1 (Mar. 2001), 17--41. Google ScholarDigital Library
- Wand, Y. and Wang, R. Y. Anchoring data quality dimensions in ontological foundations. Communications of ACM, 39, 11 (Nov. 1996), 86--95. Google ScholarDigital Library
- Vassiliadis, P., Bouzeghoub, M. and Quix, C. Towards Quality-oriented Data Warehouse Usage and Evolution. Information Systems, 25, 2 (Apr. 2000), 89--115. Google ScholarDigital Library
- Wang, R. Y. and Strong, D. M. Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12, 4 (Mar. 1996), 5--33. Google ScholarDigital Library
- Orr, K. Data quality and systems theory. Communications of ACM, 41, 2 (Feb. 1998), 66--71. Google ScholarDigital Library
- Wang, R. Y., Reddy, M. P. and Kon, H. B. Toward quality data: An attribute-based approach. Decision Support Systems, 13, 3-4 (Mar. 1995), 349--372. Google ScholarDigital Library
- Pipino, L. L., Lee, Y. W. and Wang, R. Y. Data Quality Assessment. Communications of the ACM, 45, 4 (Apr. 2002), 211--218. Google ScholarDigital Library
- Boehm, B. and In, H. Identifying Quality-Requirement Conflicts. IEEE Software, 13, 2 (Mar. 1996), 25--35. Google ScholarDigital Library
- Wang, R. Y., Kon, H. B. and Madnick, S. E. Data Quality Requirements Analysis and Modeling. In Proceedings of the Proceedings of the Ninth International Conference on Data Engineering (1993). Google ScholarDigital Library
- Jeusfeld, M. A., Quix, C. and Jarke, M. 1998. Design and Analysis of Quality Information for Data Warehouses. In Conceptual Modeling -- ER '98,T. W. Ling, S. Ram and M. L. Lee Ed. Springer-Verlag, Berlin Heidelberg, 349--362. Google ScholarDigital Library
- Edelstein, H. Planning and Designing the Data Warehouse. Simon and Schuster, 1996. Google ScholarDigital Library
- Wang, R. Y. A Product Perspective on Total Data Quality Management. Communications of the ACM, 41, 2 (Feb. 1998), 58--65. Google ScholarDigital Library
- Cappiello, C., Francalanci, C., Pernici, B., Plebani, P. and Scannapieco, M. Data Quality Assurance in Cooperative Information Systems: A muliti-dimensional Quality Certificate. City, 2003.Google Scholar
- Kimball, R., Ross, M., Thornthwaite, W., Mundy, J. and Becker, B. The Data Warehouse Lifecycle Toolkit. John Wiley & Sons, Inc, New York, 2008. Google ScholarDigital Library
- Oivo, M. and Basili, V. R. Representing Software Engineering Models: The TAME Goal Oriented Approach. IEEE Transactions on Software Engineering, 18, 10 (Oct. 1992), 886--898. Google ScholarDigital Library
- Ang, J. and Teo, T. S. H. Management Issues in Data Warehousing: Insights from the Housing and Development Board. Decision Support Systems, 29, 1 (Jul. 2000), 11--20. Google ScholarDigital Library
- Ramamurthy, K., Sen, A. and Sinha, A. P. An Empirical Investigation of the Key Determinants of Data Warehouse Adoption. Decision Support Systems, 44, 4 (Mar. 2008), 817--841. Google ScholarDigital Library
- March, S. T. and Hevner, A. R. Integrated Decision Support Systems: A Data Warehousing Perspective. Decision Support Systems, 43, 3 (Apr. 2007), 1031--1043. Google ScholarDigital Library
- Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R. and Carlsson, C. Past, Present, and Future of Decision Support Technology. Decision Support Systems, 33, 2 (Jun. 2002), 111--126. Google ScholarDigital Library
- Eckerson, W. W. Data Warehousing Special Report: Data quality and the Bottom Line. The Data Warehousing Institute, 2002.Google Scholar
- Howard, P. Pervasive Data Quality: Improving business processes with high quality data. Bloor Research London, UK, 2009.Google Scholar
- Rao, L. and Osei-Bryson, K.-M. An approach for incorporating quality-based cost--benefit analysis in data warehouse design. Information Systems Frontiers, 10, 3 (May. 2008), 361--373. Google ScholarDigital Library
Index Terms
- Quality factory and quality notification service in data warehouse
Recommendations
Towards Data Quality into the Data Warehouse Development
DASC '11: Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure ComputingCommonly, DW development methodologies, paying little attention to the problem of data quality and completeness. One of the common mistakes made during the planning of a data warehousing project is to assume that data quality will be addressed during ...
Data Warehouse Quality Assessment Using Contexts
WISE 2016: Proceedings of the 17th International Conference on Web Information Systems Engineering - Volume 10042Data Warehousing Systems DWS are of great relevance for supporting decision making and data analysis. This has been proven over time, through the generalization of its development and use in all kind of organizations. Many researchers have presented the ...
Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach
AbstractTo make informed decisions, managers establish data warehouses that integrate multiple data sources. However, the outcomes of the data warehouse-based decisions are not always satisfactory due to low data quality. Although many studies focused on ...
Comments