Automating the approximate record-matching process

Information sciences

Volumn 126, Issue 1, 2000, Pages 83-98

  Verykios, Vassilios S ^a   Elmagarmid, Ahmed K ^b   Houstis, Elias N ^b  

^a Drexel University   (United States)

^b Purdue University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; COMPUTATIONAL COMPLEXITY; DATA MINING; DATABASE SYSTEMS; ERROR ANALYSIS; PATTERN MATCHING;

DATA QUALITY; INFERENTIAL COMPLEXITY; RECORD MATCHING PROCESS;

RECORDS MANAGEMENT;

EID: 0034228352     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(00)00013-X     Document Type: Article

References (20)

1. Bitton D., DeWitt D. Duplicate record elimination in large data files. ACM Trans. Database Systems. 8(2):1983;255-265.
2. M. Cochinwala, V. Kurien, G. Lalk, D. Shasha, Efficient Data Reconciliation, Bellcore, 1998.
3. A.K. Elmagarmid, B. Horowitz, G. Karabatis, A. Umar, Issues in Multisystem Integration for Achieving Data Reconciliation and Aspects of Solutions, Technical report, Bellcore Research, 1996.
4. Fellegi I., Sunter A. A theory for record linkage. J. Amer. Statist. Asoc. 64(328):1969;1183-1210.
5. J.C. Giarratano, CLIPS user's guide, version 5.1. NASA Lyndon B. Johnson Space Center, 1991.
6. Georgakopoulos D., Karabatis G., Gantimahapatruni S. Specification and management of interdependent data in operational systems and data warehouses. Distributed and Parallel Databases. 5(2):1997;121-166.
7. M.A. Hernadez, S.J. Stolfo, Real-world data is dirty: data cleansing and the merge/purge problem, J. Data Mining and Knowledge Discovery 1 (2) 1998.
8. R. Kohavi, D. Sommerfield, J. Dougherty, Data mining using MLC++: a machine learning library in C++, in tools with artificial intelligence, IEEE Computer Society Press, 1996, http://www.sgi.com/Technology/mlc.
9. Manber U. Introduction to Algorithms. 1989;Addison-Wesley, Reading, MA.
10. A. Monge, C. Elkan, An Efficient Domain Independent Algorithm for Detecting Approximate, in: Proceedings of the 1997 SIGMOD Workshop on Research Issues on DMKD, 1997, pp. 23-29.
11. A. Motro, I. Rakov, Not all answers are equally good: estimating the quality of database answers, in: T. Andreasen et al. (Ed.), Flexible Query-Answering Systems, Kluwer Academic Publishers, Hingham, MA, 1997.
12. Newcombe H.B. Record linking: the design of efficient systems for linking records into individual and family histories. Amer. J. Human Genetics. 19(3):1967;335-339.
13. Ousterhout J.K. Scripting: higher level programming for the century. Computer. 31:1998;23-30.
14. Quinlan J.R. Induction of decision trees. Machine Learning. 1(1):1986;81-106.
15. J.R. Quinlan, Generating Production Rules From Decision Trees, in: Proceedings of tenth International Joint Conference on Artificial Intelligence, 1987, pp. 304-307.
16. Quinlan J.R. Simplifying decision trees. Internat. J. Man-Machine Stud. 27:1987;221-234.
17. Quinlan J.R. Learning decision tree classifiers. ACM Comput. Surveys. 28(1):1996;71-72.
18. W.E. Winkler, Record Linkage Software, US Bureau of the Census, 1999.
19. Wang R.Y., Storey V.C., Firth C.P. A framework for analysis of data quality research. IEEE Trans. Knowledge and Data Eng. 7(4):1995;623-640.
20. Wand Y., Wang R.Y. Anchoring data quality dimensions in ontological foundations. Comm. ACM. 39(11):1996;86-95.