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1.

図書

図書
Michael Jaedicke
出版情報: Berlin : Springer, c2001  xi, 161 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 2169
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Introduction / Chapter 1:
ORDBMS: The Next Great Wave / 1.1:
Extensible DBMS / 1.2:
Overview / 1.3:
Background on User-Defined Routines / Chapter 2:
User-Defined Routines / 2.1:
Definition, Implementation, and Execution of New UDR / 2.2:
User-Defined Scalar Functions / 2.2.1:
User-Defined Aggregate Functions / 2.2.2:
User-Defined Table Functions / 2.2.3:
User-Defined Functions and Large Objects / 2.2.4:
Comparison with Stored Procedures / 2.3:
Optimization of Queries with UDF / 2.4:
Parallel Processing of User-Defined Functions / Chapter 3:
Limits of Current ORDBMS / 3.1:
Parallel Processing of UDF / 3.3:
Two Step Parallel Aggregation of UDAF / 3.3.1:
Partitioning Classes and Partitionable Functions / 3.3.2:
Parallel Sorting as a Preprocessing Step for UDAF / 3.3.3:
Extended Syntax for Function Registration / 3.3.4:
Example Applications / 3.4:
The UDAF Most_Frequent / 3.4.1:
The UDSF Running_Average / 3.4.2:
The UDAF Median / 3.4.3:
Further Applications / 3.4.4:
Plausibility Considerations Regarding Performance / 3.5:
Related Work / 3.6:
Summary / 3.7:
Intra-function Parallelism / Chapter 4:
Compose/Decompose Operators for Intra-function Parallelism / 4.1:
Compose/Decompose Operators / 4.2.1:
Extensibility of Compose Operators by Combine Functions / 4.2.2:
Application of Intra-function Parallelism / 4.2.3:
Intra-function Parallelism for Function Pipelines / 4.2.4:
Experimental Performance Study / 4.3:
Experimental Scenario and Implementation / 4.3.1:
Performance Results / 4.3.2:
The Multi-operator Method / 4.4:
Performance Problems with Complex UDF in Current ORDBMS / 5.1:
The PBSM Algorithm as a Sophisticated UDP Implementation / 5.2.1:
The Multi-operator Method as a New Technique to Implement Complex UDF / 5.3:
The Multi-operator Method and Its Benefits / 5.3.1:
A Multi-operator Implementation of the PBSM Algorithm / 5.3.2:
Supporting the Multi-operator Method / 5.4:
Executing Query Execution Plans / 5.4.1:
Example for a Textual Specification of Query Execution Plans / 5.4.2:
Parallel Evaluation / 5.4.3:
Performance Evaluation / 5.5:
Experimental Scenario / 5.5.1:
User-Defined Table Operators / 5.5.2:
A Generalization Relationship for Row Types / 6.1:
Defining and Implementing UDTO / 6.2.2:
The Different Usages of the UDTO Concept / 6.2.3:
Parallel Processing of Procedural UDTO / 6.2.4:
Extension to Multiple Output Tables / 6.2.5:
Example Applications for UDTO / 6.3:
Computing a Spatial Join / 6.3.1:
Different UDTO for the Same Predicate / 6.3.2:
Computing the Median: An Aggregation Operator / 6.3.3:
A UDTO for a Complex Aggregation / 6.3.4:
Association Rule Mining / 6.3.5:
Summary and Conclusions / 6.4:
Implementation of UDTO / Chapter 7:
The MIDAS Prototype / 7.1:
Architectural Overview / 7.2.1:
Query Compilation and Execution / 7.2.2:
The MIDAS System Tables / 7.2.3:
UDSF in MIDAS / 7.2.4:
Implementation of SQL Macros / 7.3:
DDL Statements / 7.3.1:
SQL Macro Expansion in DML Statements / 7.3.2:
Expanding SQL Macros in Preprocessors and Middleware / 7.3.3:
Implementation of Procedural UDTO / 7.4:
Extensions to the SQL Compiler / 7.4.1:
Extensions to the Optimizer and the Parallelizer / 7.4.2:
Extensions to the Scheduler / 7.4.3:
Extensions to the Execution Engine / 7.4.4:
Extensions to Transaction Management / 7.4.5:
Implementation of Input and Output Tables / 7.4.6:
Optimization Issues for UDTO / 7.5:
UDTO and Implied Predicates / 7.5.1:
Estimating Costs and Selectivity of UDTO / 7.5.2:
Application of Traditional Optimization Rules / 7.5.3:
Using UDTO to Generate Alternative Execution Plans for UDF / 7.6:
Evaluation of the Implementation / 7.7:
Evaluation of SQL Macros / 7.7.1:
Evaluation of Procedural UDTO / 7.7.2:
Summary, Conclusions, and Future Work / 7.8:
Conclusions / 8.1:
Future Work / 8.3:
References
Appendix A
The Program sequential_invert / A.1:
The Program parallel_invert / A.2:
The Query Execution Plan for the Spatial Join with SQL Macro / A.3:
Introduction / Chapter 1:
ORDBMS: The Next Great Wave / 1.1:
Extensible DBMS / 1.2:
2.

図書

図書
by Robert J. Hilderman, Howard J. Hamilton
出版情報: Boston, MA : Kluwer Academic Publishers, c2001  xvii, 162 p. ; 25 cm
シリーズ名: The Kluwer international series in engineering and computer science ; SECS 638
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List of Figures
List of Tables
Preface
Acknowledgments
Introduction / 1.:
KDD in a Nutshell / 1.1:
The Mining Step / 1.1.1:
The Interpretation and Evaluation Step / 1.1.2:
Objective of the Book / 1.2:
Background and Related Work / 2.:
Data Mining Techniques / 2.1:
Classification / 2.1.1:
Association / 2.1.2:
Clustering / 2.1.3:
Correlation / 2.1.4:
Other Techniques / 2.1.5:
Interestingness Measures / 2.2:
Rule Interest Function / 2.2.1:
J-Measure / 2.2.2:
Itemset Measures / 2.2.3:
Rule Templates / 2.2.4:
Projected Savings / 2.2.5:
I-Measures / 2.2.6:
Silbershatz and Tuzhilin's Interestingness / 2.2.7:
Kamber and Shinghal's Interestingness / 2.2.8:
Credibility / 2.2.9:
General Impressions / 2.2.10:
Distance Metric / 2.2.11:
Surprisingness / 2.2.12:
Gray and Orlowska's Interestingness / 2.2.13:
Dong and Li's Interestingness / 2.2.14:
Reliable Exceptions / 2.2.15:
Peculiarity / 2.2.16:
A Data Mining Technique / 3.:
Definitions / 3.1:
The Serial Algorithm / 3.2:
General Overview / 3.2.1:
Detailed Walkthrough / 3.2.2:
The Parallel Algorithm / 3.3:
Complexity Analysis / 3.3.1:
Attribute-Oriented Generalization / 3.4.1:
The All_Gen Algorithm / 3.4.2:
A Comparison with Commercial OLAP Systems / 3.5:
Heuristic Measures of Interestingness / 4.:
Diversity / 4.1:
Notation / 4.2:
The Sixteen Diversity Measures / 4.3:
The I[subscript Variance] Measure / 4.3.1:
The I[subscript Simpson] Measure / 4.3.2:
The I[subscript Shannon] Measure / 4.3.3:
The I[subscript Total] Measure / 4.3.4:
The I[subscript Max] Measure / 4.3.5:
The I[subscript McIntosh] Measure / 4.3.6:
The I[subscript Lorenz] Measure / 4.3.7:
The I[subscript Gini] Measure / 4.3.8:
The I[subscript Berger] Measure / 4.3.9:
The I[subscript Schutz] Measure / 4.3.10:
The I[subscript Bray] Measure / 4.3.11:
The I[subscript Whittaker] Measure / 4.3.12:
The I[subscript Kullback] Measure / 4.3.13:
The I[subscript MacArthur] Measure / 4.3.14:
The I[subscript Theil] Measure / 4.3.15:
The I[subscript Atkinson] Measure / 4.3.16:
An Interestingness Framework / 5.:
Interestingness Principles / 5.1:
Summary / 5.2:
Theorems and Proofs / 5.3:
Minimum Value Principle / 5.3.1:
Maximum Value Principle / 5.3.2:
Skewness Principle / 5.3.3:
Permutation Invariance Principle / 5.3.4:
Transfer Principle / 5.3.5:
Experimental Analyses / 6.:
Evaluation of the All_Gen Algorithm / 6.1:
Serial vs Parallel Performance / 6.1.1:
Speedup and Efficiency Improvements / 6.1.2:
Evaluation of the Sixteen Diversity Measures / 6.2:
Comparison of Assigned Ranks / 6.2.1:
Analysis of Ranking Similarities / 6.2.2:
Analysis of Summary Complexity / 6.2.3:
Distribution of Index Values / 6.2.4:
Conclusion / 7.:
Areas for Future Research / 7.1:
Appendices
Ranking Similarities
Summary Complexity
Index
List of Figures
List of Tables
Preface
3.

図書

図書
Efraim Turban, Jay E. Aronson
出版情報: Upper Saddle River, N.J. : Prentice Hall, c1998  xxi, 890 p. ; 26 cm
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4.

図書

図書
Dieter Fensel
出版情報: Berlin : Springer, c2000  xii, 153 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1791 . Lecture notes in artificial intelligence
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5.

図書

図書
Hector J. Levesque and Gerhard Lakemeyer
出版情報: Cambridge, Mass. : MIT Press, c2000  xviii, 282 p. ; 24 cm
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Preface
Acknowledgments
Introduction / 1:
A First-Order Logical Language / 2:
An Epistemic Logical Language / 3:
Logical Properties of Knowledge / 4:
The TELL and ASK Operations / 5:
Knowledge Bases as Representations of Epistemic States / 6:
The Representation Theorem / 7:
Only-Knowing / 8:
Only-Knowing and Autoepistemic Logic / 9:
On the Proof Theory of OL / 10:
Only-Knowing-About / 11:
Avoiding Logical Omniscience / 12:
The Logic EOL / 13:
Knowledge and Action / 14:
Epilogue
References
Index
Preface
Acknowledgments
Introduction / 1:
6.

図書

図書
Pierre Cointe (ed.)
出版情報: Berlin ; Tokyo : Springer-Verlag, c1999  xi, 272 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1616
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7.

図書

図書
Behnam Azvine, Nader Azarmi, Detlef D. Nauck (eds.)
出版情報: Berlin : Springer, c2000  xvii, 357 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1804 . Lecture notes in artificial intelligence
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8.

図書

図書
Eugene Roventa and Tiberiu Spircu
出版情報: [Berlin] : Springer, c2009  xiii, 253 p. ; 24 cm
シリーズ名: Studies in fuzziness and soft computing ; v. 227
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Preface
Notations
"Classical" Expert Systems / 1:
Production Rules / 1.1:
Expert Systems / 1.2:
Structure of Rule-Based Expert Systems / 1.3:
Reasoning in an Expert System / 1.4:
Conflicts Resolution / 1.5:
Solved Exercises / 1.6:
Knowledge Representation / 2:
Data, Information and Knowledge / 2.1:
Logical Systems / 2.2:
Predicate Calculus / 2.3:
Inference Rules in Classical Logic / 2.4:
Semantic Nets / 2.5:
Frames / 2.6:
Uncertainty and Classical Theory of Probability / 2.7:
Taxonomy of Imperfection / 3.1:
Usual and Precise Meaning / 3.2:
Experiments and Events / 3.3:
Formal Definition of Events / 3.4:
Defining Probabilities / 3.5:
Defining Probabilities (II) / 3.6:
Bayes' Theorem / 3.7:
Misleading Aspects / 3.8:
Random Variables and Distributions / 3.9:
Expectation and Variance / 3.10:
Examples of Discrete Distributions / 3.11:
Continuous Distributions / 3.12:
Examples of Continuous Distributions. Normal / 3.13:
Examples of Continuous Distributions. Chi-square / 3.14:
Student and Fisher-Snedecor Distributions / 3.15:
Formal definition of Random Variables / 3.16:
Probabilities of Formulas / 3.17:
Statistical Inference / 3.18:
Inferring Scientific Truth: Tests of Significance / 4.1:
Relation "Alternative Hypothesis - Null Hypothesis" / 4.2:
Hypothesis Testing, the Classical Approach / 4.3:
Examples: Comparing Means / 4.4:
Comparing Means, the Practical Approach / 4.5:
Paired and Unpaired Tests / 4.6:
Example: Comparing Proportions / 4.7:
Goodness-of-Fit: Chi-square / 4.8:
Other Goodness-of-Fit Tests / 4.9:
Nonparametric Tests. Wilcoxon/Mann-Whitney / 4.10:
Analysis of Variance / 4.11:
Summary / 4.12:
Bayesian (Belief) Networks / 4.13:
Uncertain Production Rules / 5.1:
Bayesian (Belief, Causal) Networks / 5.2:
Examples of Bayesian Networks / 5.3:
Software / 5.4:
Bias of the Bayesian (Probabilistic) Method / 5.5:
Certainty Factors Theory / 5.6:
Certainty Factors / 6.1:
Stanford Algebra / 6.2:
Certainty Factors and Measures of Belief and Disbelief / 6.3:
Belief Theory / 6.4:
Belief Approach / 7.1:
Agreement Measures / 7.2:
Dempster-Shafer Theory / 7.3:
The Pignistic Transform / 7.4:
Combining Beliefs. The Dempster's Formula / 7.5:
Difficulties with Dempster-Shafer's Theory / 7.6:
Specializations and the Transferable Belief Model / 7.7:
Conditional Beliefs and the Generalized Bayesian Theorem / 7.8:
Possibility Theory / 7.9:
Necessity and Possibility Measures / 8.1:
Conditional Possibilities / 8.2:
Exercises / 8.3:
Approximate Reasoning / 9:
Fuzzy Sets, Fuzzy Numbers, Fuzzy Relations / 9.1:
Fuzzy Propositions and Fuzzy Logic / 9.2:
Hedges / 9.3:
Fuzzy Logic / 9.4:
Defuzzification / 9.5:
Approach by Precision Degrees / 9.7:
Review / 9.8:
Review of Uncertainty and Imprecision / 10.1:
Perception-Based Theory / 10.2:
References / 10.4:
Index
Preface
Notations
"Classical" Expert Systems / 1:
9.

図書

図書
Peter Graf
出版情報: Berlin ; New York ; Tokyo : Springer-Verlag, c1995  xiv, 284 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1053 . Lecture notes in artificial intelligence
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10.

図書

図書
Zili Zhang, Chengqi Zhang
出版情報: Berlin : Springer, c2004  xiv, 196 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 2938 . Lecture notes in artificial intelligence
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