Donald P. Ballou
- 1 Ballou, D., and Pazer, H. Modeling data and process quality in multiinput, multi-output information systems. Management Science 3i, 2 (Feb. 1985), 150-162.Google Scholar
Digital Library
- 2 Ballou, D., and Pazer, H. Designing information systems to optimize the accuracy-timeliness trade-off. Information Systems Research 6, 1 (Mar. 1995), 51-72.Google ScholarDigital Library
- 3 Ballou, D., and Tayi, G. Methodology for allocating resources for data quality enhancement. Commun. ACM 32, 3 (Mar. 1989), 320-329. Google ScholarDigital Library
- 4 Celko, J., and McDonald, J. Don't warehouse dirty data. Datamation 4i, 19 (Oct. 1995), 42-53.Google Scholar
- 5 Cushing, B. A mathematical approach to the analysis and design of internal control systems. Accounting Review 49, 1 (Jan. 1974), 24-41.Google Scholar
- 6 Laudon, K. Data quality and due process in large interorganizational record systems. Commun. ACM 29, 1 (Jan. 1986), 4-18. Google ScholarDigital Library
- 7 Morey, R.C. Estimating and improving the quality of information in an MIS. Commun. ACM25, 5 (May 1982), 337-342. Google ScholarDigital Library
- 8 Redman, T. Data Quality: Management and Technology. Bantam Books, New York, 1992. Google ScholarDigital Library
- 9 Strong, D., and Miller, S. Exceptions and exception handling in computerized information processes. A CM Transactions on Information Systems 13, 2 (Apr. 1995), 206-233. Google ScholarDigital Library
- 10 Wand, Y. and Wang, R. Anchoring data quality dimensions ontological foundations. Commun. ACM 39, 11 (Nov. 1996), 86-95. Google ScholarDigital Library
- 11 Wang, R., Storey, V., and Firth C. A framework for analysis of data quality research. IJEJEJE Transactions on Knowledge and Data Engineering 7, 4 (Aug. 1995), 623-640. Google ScholarDigital Library
- 12 Wang, R.Y. and Strong, D.M. Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems i2, 4 (Spring 1996), 5-34. Google ScholarDigital Library
Index Terms
- Enhancing data quality in data warehouse environments
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