Arnon Rosenthal
Arnon Rosenthal has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Chris Clifton
Purdue University
1 shared publication Dick Tsur
Hitachi Corp
1 shared publication- Jeffrey D. Ullman
Stanford University, Stanford, CA, USA
1 shared publication - Marianne Winslett
University of Illinois at Urbana-Champaign
1 shared publication Rajeev Motwani
Stanford University
1 shared publicationSerge Abiteboul
Stanford University and INRIA
1 shared publication
Latest Publications
- Open Access
- 13 June 2004
- Published by Association for Computing Machinery (ACM)
- p. 962-964
Security of shared data in large systems
- 13 June 2004
- Published by Association for Computing Machinery (ACM)
- p. 962-964
Open AccessSecurity of shared data in large systems
- Open Access
- 1 June 1998
- Published by Association for Computing Machinery (ACM) in ACM SIGMOD Record
- Vol. 27 (2) , 1-12
Query flocks
Association-rule mining has proved a highly successful technique for extracting useful information from very large databases. This success is attributed not only to the appropriateness of the objectives, but to the fact that a number of new query-optimization ideas, such as the “a-priori” trick, make association-rule mining run much faster than might be expected. In this paper we see that the same tricks can be extended to a much more general context, allowing efficient mining of very large databases for many different kinds of patterns. The general idea, called “query flocks,” is a generate-and-test model for data-mining problems. We show how the idea can be used either in a general-purpose mining system or in a next generation of conventional query optimizers.
- 1 June 1998
- Published by Association for Computing Machinery (ACM) in ACM SIGMOD Record
- Vol. 27 (2) , 1-12
Open AccessQuery flocks
Association-rule mining has proved a highly successful technique for extracting useful information from very large databases. This success is attributed not only to the appropriateness of the objectives, but to the fact that a number of new query-optimization ideas, such as the “a-priori” trick, make association-rule mining run much faster than might be expected. In this paper we see that the same tricks can be extended to a much more general context, allowing efficient mining of very large databases for many different kinds of patterns. The general idea, called “query flocks,” is a generate-and-test model for data-mining problems. We show how the idea can be used either in a general-purpose mining system or in a next generation of conventional query optimizers.