Data quality management using business process modeling

Proceedings - 2006 IEEE International Conference on Services Computing, SCC 2006

Volumn , Issue , 2006, Pages 398-405

  Bagchi, Sugato ^a   Bai, Xue ^b   Kalagnanam, Jayant ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COST EFFECTIVENESS; DECISION MAKING; ERROR DETECTION; MATHEMATICAL MODELS; QUALITY MANAGEMENT; SHAREHOLDERS;

BUSINESS PROCESS MODELING; DATA QUALITY MANAGEMENT; INFORMATION PROCESSING SYSTEM; REVENUE MANAGEMENT PROCESS;

INFORMATION MANAGEMENT;

EID: 35148840722     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SCC.2006.41     Document Type: Conference Paper

References (19)

1. Ballou, D., Wang, R., Pazer, H., and Tayi, G. Modeling Information Manufacturing Systems to Determine Information Product Quality. Management Science (44:4), April 1998, pp. 462-484.
2. Cappiello, C., Francalanci, C., and Pernici, B..Data quality assessment from the user's perspective. In proceedings of International Workshop on Information Quality in Information Systems 2004.
3. Cushing, B. E., A Mathematic Approach of the Analysis and Design of Internal Control Systems. The Accounting Review 1974, 24-41.
4. Hamlen, S. S., A chance-Constrained Mix Integer Programming Model for Internal Control Systems. The Accounting Review 1980, 578-593.
5. Ham, J., Losell D., and Smieliauskas W. An Empirical Study of Error Characteristics in Accounting Populations. The Accounting Review Vol. 60, No. 3 (Jul., 1985), pp. 387-406.
6. IBM Whitepaper, "Leverage IBM Lotus Workplace Capabilities to Monitor and Manage both Business Operations and Systems," March 2004.
7. Paul Mason, "Business Performance Management: Merging Business Optimization with IT Optimization," IDC Whitepaper, March 2004. Available from ftp://ftp.software.ibm.com/software/tivoli/analystreports/ar-bpm.pdf
8. Knechel, W. R. The use of Quantitative Models in the Review and Evaluation of Internal Control: A Survey and Review. Journal of Accounting Literature, (Vol. 2), Spring 1983: 205-219.
9. Krishnan, R., Peters, J., Padman, R., and Kaplan, D. 2005. On Data Reliability Assessment in Accounting Information Systems. Information System Research Vol. 16, No. 3, September 2005, 307-326.
10. Laws, D. and Hagan A. O.. Bayesian Inference for Rare Error in Population with Unequal Unit Sizes", Applied Statistics, 49:4 (2000) 577-590.
11. Lea, R.B., Adams, S.J., and Boykin R.F. Modeling of the audit risk assessment process at the assertion level within an account balance. Auditing: A Journal of Practice & Theory, 1992 (Vol. 11, Supplement): 152-179.
12. Martello, S. and Toth, P. Knapsack Problems, Algorithms and Computer Implementations 1990, John Wiley and Sons Ltd., England.
13. Nado, R., Chams, M., Delisio, J., and Hamscher, W. Comet: An Application of Model-Based Reasoning to Accounting Systems. In proceedings of the Eighth Innovative Applications of Artificial Intelligence Conference AAAI Press. 1482-1490, 1996.
14. Pierce, E. M. Assessing Data Quality with Control Matrices. Communications of the ACM, (47:2): 82-86, 2004.
15. Pipino, L. L., Lee, Y.W., and Wang., R.Y. Data Quality Assessment. Communications of the ACM, (45:4): 211-218, 2002.
16. Strong, D., Lee, Y. W., and Wang, R.Data Quality in Context. Communications of the ACM, (40:5): 58-65, 1997.
17. Waller, W. S. Auditors' assessments of inherent and control risk in field settings. The Accounting Review 68 (October): 783-803, 1993.
18. Wand, Y. and Wang, R. Y. Anchoring data quality dimensions in ontological foundations. Communication of the ACM (39: 11) 86-95, 1996.
19. Wang R. A Product prospective on Total Data Quality Management. Communications of the ACM, (41:2): 58-65, 1995.