- 1 Ballou, D. P. and Pazer, H. L. Modeling data and process quality in multi-input, multi-output information systems. Manage. Sci. 31, 2 (1985), pp. 150-162.Google ScholarDigital Library
- 2 Ballou, D. P. and Tayi, K. G. Methodology for allocating resources for data quality enhancement. Commun. ACM 32, 3 (1989), pp. 320-329. Google ScholarDigital Library
- 3 Ballou, D. P., Wang, R. Y., Pazer, H., and Tayi, K. G. Modeling information manufacturing systems to determine information product quality. Manage. Sci. (accepted for publication, 1996). Google ScholarDigital Library
- 4 Deming, E. W. Out oft he Crisis. MIT Center for Advanced Engineering Study. Cambridge, Mass. 1986.Google Scholar
- 5 Laudon, K. C. Data quality and due process in large interorganizational record systems. Commun. ACM 29, 1 (1986), pp. 4-11. Google ScholarDigital Library
- 6 Liepins, G. E. and Uppuluri, V. R. R., Eds. Data Quality Control: Theory and Pragmatics. D. B. Owen, (1990), Marcel Dekker, New York, N.Y. Google ScholarDigital Library
- 7 Morey, R. C. Estimating and improving the quality of information in MIS. Commun. ACM 25, 5 (1982), pp. 337-342. Google ScholarDigital Library
- 8 Wand, Y. and Wang, R. Y. Anchoring data quality dimensions in ontological foundations. Commun. ACM 39, 11 (1996), pp.86-95. Google ScholarDigital Library
- 9 Wang, R. Y. and Kon, H. B. Towards Total Data Quality Management (TDQM). Information Technology in Action: Trends and Perspectives. R. Y. Wang, Ed. 1993. Prentice Hall, Englewood Cliffs, NJ. Google ScholarDigital Library
- 10 Wang, R. Y., Storey, V. C. and Firth, C. P. A framework for analysis of data quality research. IEEE Trans. Know. Data Eng. 7, 4 (1995), pp. 623-64O. Google ScholarDigital Library
- 11 Wang, R. Y. and Strong, D. M. Beyond accuracy: What data quality means to data consumers. J. Manage. Info. Syst. 12, 4 (1996), pp. 5-34. Google ScholarDigital Library
- 12 Weber, R. EDP Auditing: Conceptual Foundations and Practices. G. B. Davis, Ed. 1988. McGraw-Hill, New York, N.Y. Google ScholarDigital Library
Index Terms
- Data quality in context
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