Ex ante evaluations of alternate data structures for end user queries: Theory and experimental test

Journal of Database Management

Volumn 15, Issue 4, 2004, Pages 45-70

  Bowen, Paul L ^a,b   Rohde, Fiona H ^a,b   Basford, Jay ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Complexity metrics; Data structure evaluation; End user queries

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA REDUCTION; DATA STRUCTURES; INFORMATION ANALYSIS; QUERY LANGUAGES; SOCIETIES AND INSTITUTIONS; USER INTERFACES;

COMPLEXITY METRICS; DATA STRUCTURE EVALUATION; END USER QUERIES;

DATABASE SYSTEMS;

EID: 4444310059     PISSN: 10638016     EISSN: None     Source Type: Journal    
DOI: 10.4018/jdm.2004100103     Document Type: Article

References (38)

1. Benedict, C., Bowen, P., Ferguson, C., & Rohde, F. (1997). The effects of normalization on the satisfaction of novice end-users querying databases. Australian J. of Infor. Systems, 4(2), 4-24.
2. Borthick, A.F., Bowen, P.L., Liew, S.T., & Rohde, F.H. (2001). The effects of normalization and complexity on end-user query errors: An experimental evaluation. Int. Journal of Accounting Information Systems, 2, 1-29.
3. Campbell, D. J. (1988). Task complexity: A review and analysis. Academy of Management Review, 13(1), 40 52.
4. Ceri, S., & Ramakrishnan, R. (1996). Rules in database systems. ACM Computing Surveys, 25(1), 109-111.
5. Chan, H.C., Siau, K.L., & Wei, K.K. (1998). The effect of data model, system and task characteristics on user query performance - An empirical study. Database, 29(1), 31-49.
6. Chan, H.C., Tan, B.C.Y., & Wei, K.K. (1999). Three important determinants of user performance for database retrieval. International Journal of Human-Computer Studies, 51, 895-918.
7. Connolly, T.M., & Begg, C.E. (2002). Database systems: A practical approach to design, implementation, and management (3 ed.).New York: Addison-Wesley.
8. th ed.). Reading, MA: Addison-Wesley.
9. Delligatta, A., & Umbaugh, R.E. (1993). EUC becomes enterprise computing. Info. Systems Manag., 10(4), 53-55.
10. Dyer, M. (1992). The cleanroom approach to quality software development. New York: John Wiley.
11. Elmasri, R., & Navathe, S.B. (2004). Fundamentals of database systems (4th ed.). New York: Addison-Wesley.
12. Fisher, C.W., Chengalur Smith, I., & Ballou, D.P. (2003). The impact of experience and time on the use of data quality information in decision making. Information Systems Research, 14(2), 170-88.
13. Gardner, W.B., & Serra, M. (1997, January). An object-oriented layered approach to interfaces for hardware/software codesign of embedded systems. The 31st Annual Hawaii International Conference on System Sciences (vol. 7: Software technology, pp. 197-203).
14. Hall, T.W., Hunton, J.E., & Pierce, B.J. (2002). Sampling practices of auditors in public accounting, industry and government. Accounting Horizons, 16, 125-136.
15. Halstead, M.H. (1977). Elements of software science. North-Holland: Elsevier.
16. Harrison, R., Samaraweera, L.G., Dobie, M.R., & Lewis, P.H. (1996). An evaluation of code metrics for object-oriented programs. Information & Software Technology, 38(7), 443-450.
17. Hilton, R.W., Swieringa, R.J., & Hoskin, R.E. (1981). Perception of accuracy as a determinant of information value. J. of Accounting Research, 19, 86-108.
18. th ed.). NJ: Prentice-Hall.
19. Howden, W.E. (1986). A functional approach to program testing. IEEE Transactions on Software Engineering, 12(10), 290-309.
20. th ed.). New York: McGraw-Hill.
21. Klein, B.D. (2002). Some evidence on the detection of data errors. Advanced Topics in Infor. Res. Man., 1, 279-95.
22. Lamb, R., & Kling, R. (2003). Reconceptualizing users as social actors in information systems research. MIS Quarterly, 27(2), 197-235.
23. Leitnaker, M.G., Sanders, R.D., & Hild, C. (1996). The power of statistical thinking: Improving industrialprocesses. Boston, MA: Addison-Wesley.
24. Lorence, D.P. (2003). The perils of data misreporting. Communications of the ACM, 46(11), 85-88.
25. March, J., & Simon, H.A. (1958). Organizations. New York: John Wiley.
26. McCabe, T. (1976). A complexity measure. IEEE Transactions on Software Engineering, SE-2(4), 308-320.
27. McCarthy, W. (1982). The REA accounting model: A generalized framework for accounting systems in a shared data environment. Accounting Review, 57(3), 554-578.
28. McLean, E.R., Kappelman, L.A., & Thompson, J.P. (1993). Converging enduser and corporate computing. Communications of the ACM, 36 (12), 78-92.
29. Mannino, M.V. (2001). Database application development and design. Boston: McGraw Hill.
30. Menzies, T., & Hu, Y. (2003, November). Data mining for very busy people. Computer, 22-29.
31. nd ed.). New York: John Wiley.
32. Rho, S., & March, S.T. (1997). An analysis of semantic overload in database access systems using multi-table query formulation. Journal of Database Management, 5(2), 3-14.
33. Raisinghani, M.S. (2000). Adapting data modeling techniques for data warehouse design. Journal of Computer Information Systems, 40(3), 73-77.
34. Rissanen, J. (1989). Stochastic complexity in statistical inquiry. Singapore: World Scientific.
35. Smelcer, L. (1995). User errors in database query composition. Int. J. of Human-Comp. Studies, 42, 115-129.
36. Strong, D.M., Lee, Y.W., &Wang, R.Y. (1997). Data quality in context. Communications of the ACM, 40, 103-110.
37. Topi, H., & Ramesh, V. (2002). Human factors research on data modeling: A review of prior research, an extended framework and future research directions. J. of Datab. Manage., 13(2), 3-19.
38. Wang, R.Y., & Strong, D.M. (1996). Beyond accuracy: What data quality means to data consumers. J. of Management Information Systems, 12, 5-33.