A theoretical framework for learning from a pool of disparate data sources

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2002, Pages 443-449

  Ben David, Shai ^a,b   Gehrke, Johannes ^b   Schuller, Reba ^c  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b Department of Computer Science and School of Operations Research and Information Engineering   (United States)

^c Department of Obstetrics Gynecology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; COMPUTATIONAL COMPLEXITY; CONFORMAL MAPPING; DATABASE SYSTEMS; INFORMATION ANALYSIS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS;

COMPUTATIONAL LEARNING THEORY; DATA SOURCES; THEORETICAL FRAMEWORK;

DATA PROCESSING;

EID: 0242709373     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/775107.775111     Document Type: Conference Paper

References (17)

1. J. Baxter. A model of inductive bias learning. Journal of Artificial Intelligence Research, 12:149-198, 2000.
2. A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Learnability and the vapnik-chervonenkis dimension. Journal of the Association for Computing Machinery, 36(4):929-965, 1989.
3. R. Caruana. Multitask learning. Machine Learning, 28(1):41-75, 1997.
4. S. Castano and V. D. Antonellis. A schema analysis and reconciliation tool environment for heterogeneous databases. In IDEAS, pages 53-62, 1999.
5. A. Doan, P. Domingos, and A. Y. Halevy. Reconciling schemas of disparate data sources: A machine-learning approach. In SIGMOD Conference, 2001.
6. R. Goldman and J. Widom. Dataguides: Enabling query formulation and optimization in semistructured databases. In VLDB'97, Proceedings of 23rd International Conference on Very Large Data Bases, August 25-29, 1997, Athens, Greece, pages 436-445. Morgan Kaufmann, 1997.
7. D. Hall and J. Llinas. An introduction to multisensor data fusion. In Proceedings of the IEEE, volume 85, pages 6-23.
8. M. J. Kearns and U. V. Vazirani. An Introduction to Computational Learning Theory. MIT Press, 1994.
9. D. S. Luigi Palopoli and D. Ursino. Semi-automatic semantic discovery of properties from database schemas. In IDEAS, pages 244-253, 1998.
10. T. Milo and S. Zohar. Using schema matching to simplify heterogeneous data translation. In Proc. 24th Int. Conf. Very Large Data Bases, VLDB, pages 122-133, 24-27 1998.
11. E. Rahm and P. Bernstein. On matching schemas automatically. Dept. of Computer Science, Univ. of Leipzig, 2001.
12. S. Ben-David, J. Gehrke and R. Schuller. Technical report, Computer-Science Department, Cornell University, May 2002.
13. S. Thrun and J. O'Sullivan. Discovering structure in multiple learning tasks: The TC algorithm. In International Conference on Machine Learning, pages 489-497, 1996.
14. S. Thrun and L. Pratt, editors. Learning To Learn. Kluwer Academic Publishers, November 1997.
15. H. Wache, T. Vgele, U. Visser, H. Stuckenschmidt, G. Schuster, H. Neumann, and S. Hbner. Ontology-based integration of information - a survey of existing approaches. In Proceedings of the Workshop Ontologies and Information Sharing, IJCAI, 2001.
16. L. Wald. An overview of concepts in fusion of earth data. In P. Gudmandsen, editor, Future trends in Remote Sensing, pages 385-390. Balkema, 1997.
17. Q. Y. Wang, J. X. Yu, and K.-F. Wong. Approximate graph schema extraction for semi-structured data. In Advances in Database Technology - EDBT 2000, 7th International Conference on Extending Database Technology, Konstanz, Germany, March 27-31, 2000, Proceedings, volume 1777 of Lecture Notes in Computer Science, pages 302-316. Springer, 2000.