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Abstract: xi 1 Introduction 1 1.1 Semantic Query Optimization : : : : : : : : : : : : : : : : : : : : : : 3 1.2 High Utility Semantic Knowledge for SQO : : : : : : : : : : : : : : : 6 1.3 Learning Effective and Robust Rules : : : : : : : : : : : : : : : : : : 8 1.4 Closely Related Work : : : : : : : : : : : : : : : : : : : : : : : : : : : 10 1.5 Summary of Contributions : : : : : : : : : : : : : : : : : : : : : : : : 12 1.6 Organization of the Dissertation : : : : : : : : : : : : : : : : : : : : : 13 2 ... (Update)
Context of citations to this paper: More
...to maximize the net utility of learning. Using Robustness Values for Resource Control I have developed a general solution (Hsu 1996) to deal with the tradeoff in the rule induction for SQO. The solution is to develop an approach to estimating the degree of...
...the optimizer can still be used with minimal modification. Many algorithms are available for learning useful semantic knowledge [16, 22, 19, 23, 24, 25]. 1.1 Query Plans A query plan is a directed acyclic graph with its nodes as plan steps and its edges as the ordering...
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3: Query Optimization by Semantic Reasoning (context) - King - 1984
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BibTeX entry: (Update)
Hsu, C.-N. 1996. Learning Effective and Robust Knowledge for Semantic Query Optimization. Ph.D. Dissertation, Department of Computer Science, University of Southern California. http://citeseer.ist.psu.edu/article/hsu97learning.html More
@misc{ hsu96learning,
author = "C. Hsu",
title = "Learning Effective and Robust Knowledge for Semantic Query Optimization",
text = "Hsu, C.-N. 1996. Learning Effective and Robust Knowledge for Semantic Query
Optimization. Ph.D. Dissertation, Department of Computer Science, University
of Southern California.",
year = "1996",
url = "citeseer.ist.psu.edu/article/hsu97learning.html" }
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