Hitherto, most relational database design methods are based on functional dependencies (FDs) and multivalued dependencies (MVDs). Full mappings are proposed as an alternative to FDs and MVDs. A mapping between any two sets, apart from being one-one, many-one, or many-many, is either total or partial on the source and target sets. An 'into' mapping on a set, expresses the fact that an element in the set may not be involved in the mapping. An 'onto' mapping on a set is total on the set. A many-many (into,onto) mapping from set A to set B is written as A[sup=i] m----n B[sup=o]. The mappings incorporate more semantic information into data dependency specification. It is shown, informally, that the full mappings are more expressive than FDs and MVDs. Transformation rules, to generate Boyce-Codd normal form and projection-join normal form schemas from the full mappings, are defined. The full mapping/transformation rules provide a discipline for modeling nonfunctional relationships, within a synthetic approach. / Science, Faculty of / Computer Science, Department of / Graduate

by Kwok Chak Hung. / Thesis (M.Ph.)--Chinese University of Hong Kong

譚英隆 = Chinese input systems : a comparative study of Cantonese ... / Thesis (M.A.)--香港中文大學. / Manuscript. / Tan Yinglong = Chinese input systems : a comparative study of Cantonese ... / Thesis (M.A.)--Xianggang Zhong wen da xue. / 摘要 --- p.1 / Chapter 第一章 --- 問題背景 --- p.3 / 資訊處理的重要性 --- p.3 / 資訊處理在漢字社會所遇到的困難 --- p.5 / 香港中文資訊處理的需要 --- p.7 / 中文電腦在香港的使用現況 --- p.9 / 中文輸入法在香港的現況 --- p.10 / 普及漢字輸入法的重要性 --- p.13 / 本研究的內容和目的 --- p.15 / 本研究在教育上的意義 --- p.19 / Chapter 第二章 --- 文獻評述 --- p.22 / 電腦中文化 --- p.22 / 漢字的屬性 --- p.22 / 中文輸入法的兩大形式 --- p.24 / 中文鍵盤輸入法的類別 --- p.28 / 漢字輸入編碼的種類 --- p.31 / 字根編碼輸入 --- p.33 / 角形代碼輸入 --- p.36 / 筆劃編碼輸入 --- p.38 / 香港運科國際有限公司的基本筆劃編碼法 --- p.45 / 本研究的基本筆劃編碼法 --- p.47 / 拼音輸入法 --- p.49 / 廣東拼音輸入法 --- p.57 / 廣東拼音的注意符號 --- p.59 / 香港慣用的廣東注音符號 --- p.62 / 較近代的字書所採用的廣東注音符號 --- p.64 / 超群電腦公司廣東拼音輸入法所採用的注音符號 --- p.66 / 聲調的標注 --- p.68 / 中文輸入法的評估 --- p.70 / 1984年在上海進行的中文輸入編碼評測 --- p.72 / 資訊工業策進會1983年所作的評測 --- p.73 / 1985年台灣國立清華大學方聖平等人所作的評測 --- p.76 / 1987年台灣資訊工業策進會的測試 --- p.78 / 幾次主要評測的評述 --- p.81 / Chapter 第三章 --- 研究設計 --- p.86 / 簡述 --- p.86 / 受測試的中文輸入法 --- p.86 / 受試者 --- p.87 / 研究範圍 --- p.89 / 見字編碼和不見字編碼 --- p.92 / 教學和測驗進程 --- p.93 / 廣東拼音輸入法的教學 --- p.94 / 基本筆劃輸入法的教學 --- p.95 / 測量工具 --- p.96 / 測驗字的選取 --- p.98 / 資料分析 --- p.100 / Chapter 第四章 --- 結果及討論 --- p.108 / 三次測驗的總平均成績 --- p.108 / 總平均成績的進步情況 --- p.109 / 見字編碼測驗的平均成績 --- p.111 / 見字編碼部份的進步情況 --- p.112 / 聽字編碼測驗的平均成績 --- p.113 / 聽字編碼測驗的進步情況 --- p.115 / 輸入法與測驗法的關係 --- p.117 / 兩種輸入法學習難易的比較 --- p.118 / 錯誤類型統計 --- p.120 / 錯誤出現的頻率 --- p.121 / 廣東拼音輸入法最常見的錯誤 --- p.123 / 基本筆劃輸入法的取常見錯誤 --- p.131 / 從實驗結果比較兩種輸入法的學習歷程 --- p.136 / 已有知識的比較 --- p.137 / 其他知識對學習的干擾 --- p.138 / 記憶量的比較 --- p.143 / 規則難度的比較 --- p.145 / 規則變異的比較 --- p.151 / 從使用者的不同要求看兩種輸入法不同的特性 --- p.151 / 針對兩種輸入法特點的一些改良建議 --- p.153 / 見字輸入和聽字輸入 --- p.157 / 總結 --- p.158 / Chapter 第五章 --- 研究的限制 --- p.160 / 對機操作 --- p.160 / 完整的學習曲線 --- p.161 / 受試者 --- p.161 / 研究裡所採用的教學設計 --- p.162 / Chapter 圖一 --- 三次測驗的總成績 --- p.110 / Chapter 圖二 --- 三次見字編碼測驗的成績 --- p.113 / Chapter 圖三 --- 聽字編碼測驗的成績 --- p.116 / Chapter 圖四 --- 根據學習歷史和一般學習曲線的常態推斷的基本筆劃輸入法和廣東拚音 --- p.150 / Chapter 表一 --- 三次測驗的總平均成績 --- p.109 / Chapter 表二 --- 總平均成績的進步幅度 --- p.110 / Chapter 表三 --- 見字編碼測驗的成績 --- p.111 / Chapter 表四 --- 見字編碼測驗的進步幅度 --- p.113 / Chapter 表五 --- 聽字編碼測驗的成績 --- p.115 / Chapter 表六 --- 聽字編碼測驗的進步幅度 --- p.116 / Chapter 表七 --- 兩種輸入法在不同測驗法中的成績差異 --- p.117 / Chapter 表八 --- 廣東拚音輸入法最常見的二十種錯誤 --- p.123 / Chapter 表九 --- 引致各項錯誤的原因分析 --- p.129 / Chapter 表十 --- 基本筆劃輸入法最常見的二十種錯誤 --- p.131 / Chapter 表十一 --- 出現最多的二十種錯誤和它們所牽涉的錯誤類型 --- p.134 / 參考書目 / Chapter 附錄一 --- 基本筆劃輸入法教案 / Chapter 附錄二 --- 廣東拼音輸入法教案 / Chapter 附錄三 --- 測驗題目樣本 / Chapter 附錄四 --- 錯誤類型統計表

System identification deals with the estimation of mathematical models from experimental data. As mathematical models are built for specific purposes, ensuring that the estimated model represents the system with sufficient accuracy is a relevant aspect in system identification. Factors affecting the accuracy of the estimated model include the experimental data, the manner in which the estimation method accounts for prior knowledge about the system, and the uncertainties arising when designing the experiment and initializing the search of the estimation method. As the accuracy of the estimated model depends on factors that can be affected by the user, it is of importance to guarantee that the user decisions are optimal. Hence, it is of interest to explore how to optimally perform an experiment in the system, how to account for prior knowledge about the system and how to deal with uncertainties that can potentially degrade the model accuracy. This thesis is divided into three topics. The first contribution concerns an input design framework for the identification of nonlinear dynamical models. The method designs an input as a realization of a stationary Markov process. As the true system description is uncertain, the resulting optimization problem takes the uncertainty on the true value of the parameters into account. The stationary distribution of the Markov process is designed over a prescribed set of marginal cumulative distribution functions associated with stationary processes. By restricting the input alphabet to be a finite set, the parametrization of the feasible set can be done using graph theoretical tools. Based on the graph theoretical framework, the problem formulation turns out to be convex in the decision variables. The method is then illustrated by an application to model estimation of systems with quantized measurements. The second contribution of this thesis is on Bayesian techniques for input design and estimation of dynamical models. In regards of input design, we explore the application of Bayesian optimization methods to input design for identification of nonlinear dynamical models. By imposing a Gaussian process prior over the scalar cost function of the Fisher information matrix, the method iteratively computes the predictive posterior distribution based on samples of the feasible set. To drive the exploration of this set, a user defined acquisition function computes at every iteration the sample for updating the predictive posterior distribution. In this sense, the method tries to explore the feasible space only on those regions where an improvement in the cost function is expected. Regarding the estimation of dynamical models, this thesis discusses a Bayesian framework to account for prior information about the model parameters when estimating linear time-invariant dynamical models. Specifically, we discuss how to encode information about the model complexity by a prior distribution over the Hankel singular values of the model. Given the prior distribution and the likelihood function, the posterior distribution is approximated by the use of a Metropolis-Hastings sampler. Finally, the existence of the posterior distribution and the correctness of the Metropolis-Hastings sampler is analyzed and established. As the last contribution of this thesis, we study the problem of uncertainty in system identification, with special focus in input design. By adopting a risk theoretical perspective, we show how the uncertainty can be handled in the problems arising in input design. In particular, we introduce the notion of coherent measure of risk and its use in the input design formulation to account for the uncertainty on the true system description. The discussion also introduces the conditional value at risk, which is a risk coherent measure accounting for the mean behavior of the cost function on the undesired cases. The use of risk coherent measures is also employed in application oriented input design, where the input is designed to achieve a prescribed performance in the intended model application. / <p>QC 20161216</p>

by Lo I Fan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references. / Chapter Chapter 1: --- Introduction / Chapter 1.1 --- The Evolution of Fonts --- p.1 / Chapter 1.2 --- Bitmap Fonts --- p.2 / Chapter 1.3 --- Outline Fonts / Chapter 1.3.1 --- Arc and Vector Form --- p.4 / Chapter 1.3.2 --- Spline Form --- p.4 / Chapter 1.3.3 --- Pros and Cons of Outline Fonts --- p.8 / Chapter 1.4 --- Examples of Outline Fonts / Chapter 1.4.1 --- Adobe's PostScript --- p.9 / Chapter 1.4.2 --- Apple's and Microsoft TrueType / Chapter 1.4.2.1 --- Outline Representation --- p.10 / Chapter 1.4.2.2 --- Rasterisation --- p.12 / Chapter 1.4.2.3 --- Hinting --- p.13 / Chapter 1.5 --- Bold Fonts / Chapter 1.5.1 --- Definition of Bold --- p.15 / Chapter 1.5.2 --- Definition of Auto B oldness --- p.16 / Chapter 1.5.3 --- Auto Boldness by Double Printing --- p.17 / Chapter 1.5.4 --- Auto Boldness by Multi-Master Technique --- p.18 / Chapter 1.6 --- Chinese Fonts / Chapter 1.6.1 --- Chinese Character Sets --- p.19 / Chapter 1.6.2 --- The Subtleties of Chinese Fonts Auto Boldness --- p.21 / Chapter 1.7 --- Project Objective --- p.23 / Chapter 1.8 --- Goals --- p.23 / Chapter Chapter 2: --- Main Ideas of Chinese Font Auto Boldness / Chapter 2.1 --- Prototype of Auto Boldness Driver --- p.24 / Chapter 2.2 --- Design Features of the Prototype Auto Boldness Driver --- p.25 / Chapter 2.3 --- Data Structure and Algorithm of Auto Boldness / Chapter 2.3.1 --- Data Structure of TrueType Character Outline --- p.27 / Chapter 2.3.2 --- Algorithm of Auto Boldness --- p.28 / Chapter 2.3.3 --- Algorithm Description --- p.29 / Chapter 2.4 --- Component Font Auto Boldness --- p.35 / Chapter Chapter 3: --- Language of Auto Boldness / Chapter 3.1 --- Enhancements of TrueType Engine to support Auto Boldness --- p.36 / Chapter 3.2 --- Symmetric Bold Instruction --- p.38 / Chapter 3.3 --- Rotate Bold Instruction --- p.47 / Chapter 3.4 --- Asymmetric B old Instruction --- p.50 / Chapter 3.5 --- Comparison of Bold Instructions --- p.54 / Chapter 3.6 --- Serif Accommodation Instruction --- p.55 / Chapter Chapter 4: --- Shape Parsing and Auto Bold Code Generation / Chapter 4.1 --- Compilation Process and Auto Boldness --- p.62 / Chapter 4.2 --- Shape Lexical Analyzer --- p.64 / Chapter 4.3 --- Shape Token Attributes Evaluation / Chapter 4.3.1 --- line Token --- p.66 / Chapter 4.3.2 --- bezier2 Token --- p.67 / Chapter 4.3.3 --- sharp Token --- p.70 / Chapter 4.3.4 --- concave Token --- p.75 / Chapter 4.3.5 --- convex Token --- p.75 / Chapter 4.4 --- Scope of Shape Parsing --- p.76 / Chapter 4.5 --- Shape Parsing Mechanism --- p.77 / Chapter 4.6 --- Model Grammar Rules / Chapter 4.6.1 --- Grammar Rule Format --- p.81 / Chapter 4.6.2 --- Grammar Rule Item --- p.82 / Chapter 4.6.3 --- Grammar Rule Assignment --- p.83 / Chapter 4.6.4 --- Grammar Rule Condition --- p.83 / Chapter 4.7 --- Auto Boldness Code Generation --- p.84 / Chapter 4.8 --- Program Methodology of Prototype Auto Boldness Driver --- p.86 / Chapter Chapter 5: --- Conclusions / Chapter 5.1 --- Work Achieved --- p.87 / Chapter 5.2 --- The Pros and Cons of Auto Boldness Algorithm --- p.88 / Chapter 5.3 --- Bold Quality Assessments --- p.91 / Chapter 5.3 --- Future Directions --- p.93 / References / Appendix One / Appendix Two

by Tong Po-Cheung. / Bibliography: leaves [v-10]-[v-11] / Thesis (M.Ph.)--Chinese University of Hong Kong, 1983

by Wong Tsz Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 45-47). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Characteristics of Chinese Character --- p.2 / Chapter 1.1.1 --- The Nature of Chinese Language --- p.2 / Chapter 1.1.2 --- The Structure of Chinese Characters --- p.3 / Chapter 1.1.3 --- Basic Writing Strokes --- p.3 / Chapter 1.1.4 --- Writing Stroke Sequencing --- p.3 / Chapter 1.1.5 --- Geographic Structure of Components --- p.4 / Chapter 1.2 --- Stroke Distribution of Chinese Characters --- p.5 / Chapter 1.3 --- Radical --- p.5 / Chapter 1.4 --- Overview --- p.6 / Chapter 1.5 --- Objective --- p.10 / Chapter 2 --- Preprocessing --- p.12 / Chapter 2.1 --- Smoothing and Sampling --- p.12 / Chapter 2.2 --- Interpolation --- p.13 / Chapter 2.3 --- Dehooking --- p.13 / Chapter 2.4 --- Normalization --- p.14 / Chapter 2.5 --- Stroke Segmentation --- p.15 / Chapter 3 --- Preclassification --- p.18 / Chapter 3.1 --- Feature Analysis --- p.18 / Chapter 3.2 --- Radical Detection --- p.20 / Chapter 3.3 --- Description of The Preclassification Component --- p.22 / Chapter 3.4 --- Results and Conclusions --- p.23 / Chapter 4 --- The Recognition Stage --- p.25 / Chapter 4.1 --- Introduction --- p.25 / Chapter 4.2 --- Stroke Match Algorithm --- p.26 / Chapter 4.3 --- Relation Match Stage --- p.30 / Chapter 4.3.1 --- Introduction --- p.30 / Chapter 4.4 --- Final Classification --- p.35 / Chapter 5 --- Results and Conclusions --- p.39 / Chapter 5.1 --- Experiment Results --- p.39 / Chapter 5.2 --- Analysis --- p.39 / Chapter 5.3 --- Conclusions

Yeung Lap Kei. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves [14-15]). / PREFACE / ABSTRACT / CONTENT / Chapter §1. --- INTRODUCTION / Chapter §1.1 --- The Chinese language --- p.1 / Chapter §1.2 --- Chinese information processing system --- p.2 / Chapter §1.3 --- Chinese character recognition --- p.4 / Chapter §1.4 --- Multi-stage tree classifier Vs Single-stage tree classifier in Chinese character recognition --- p.6 / Chapter §1.5 --- Decision Tree / Chapter §1.5.1 --- Basic Terminology of a decision tree --- p.7 / Chapter §1.5.2 --- Structure design of a decision tree --- p.10 / Chapter §1.6 --- Motivation of the project --- p.12 / Chapter §1.7 --- Objects of the project --- p.14 / Chapter §1.8 --- Development environment --- p.14 / Chapter §2. --- APPROACH 1 - UNSUPERVISED LEARNING --- p.15 / Chapter §3. --- APPROACH 2 - SUPERVISED LEARNING / Chapter §3.1 --- Idea --- p.17 / Chapter §3.2 --- The 3 Corner Code --- p.20 / Chapter §3.3 --- Feature Extraction & Selection --- p.22 / Chapter §3.4 --- Decision at Each Node / Chapter §3.4.1 --- Statistical Linear Discriminant Analysis --- p.22 / Chapter §3.4.2 --- Optimization of the Number of Misclassification --- p.24 / Chapter §3.5 --- Implementation / Chapter §3.5.1 --- Training Data --- p.36 / Chapter §3.5.2 --- Clustering with the Use of SAS --- p.38 / Chapter §3.5.3 --- Building the Decision Trees --- p.42 / Chapter §3.5.4 --- Description of the Classifier --- p.45 / Chapter §3.6 --- Experiments and Testing Result / Chapter §3.6.1 --- Performance Parameters being Measured --- p.47 / Chapter §3.6.2 --- Testing by Resubstitution Method --- p.50 / Chapter §3.6.3 --- Noise Model --- p.52 / Chapter §4. --- POSSIBLE IMPROVEMENT --- p.55 / Chapter §5. --- EXPERIMENTAL RESULTS & THE IMPROVED MULTISTAGE CLASSIFIER / Chapter §5.1 --- Experimental Results --- p.59 / Chapter §5.2 --- Conclusion --- p.70 / Chapter §6. --- IMPROVED MULTISTAGE TREE CLASSIFIER / Chapter §6.1 --- The Optimal Multistage Tree Classifier --- p.72 / Chapter §6.2 --- Performance Analysis --- p.73 / Chapter §7. --- FURTHER DISCRIMINATION BY CONTEXT CONSIDERATION / Chapter §7.1 --- Idea --- p.76 / Chapter §7.2 --- Description of Algorithm --- p.78 / Chapter §7.3 --- Performance Analysis --- p.81 / Chapter §8. --- CONCLUSION / Chapter §8.1 --- Advantage of the Classifier --- p.84 / Chapter §8.2 --- Limitation of the Classifier --- p.85 / Chapter §9. --- AREA OF FUTURE RESEARCH AND IMPROVEMENT / Chapter §9.1 --- Detailed Analysis at Each Terminal Node --- p.86 / Chapter §9.2 --- Improving the Noise Filtering Technique --- p.87 / Chapter §9.3 --- The Use of 4 Corner Code --- p.88 / Chapter §9.4 --- Increase in the Dimension of the Feature Space --- p.90 / Chapter §9.5 --- 1-Tree Protocol with Entropy Reduction --- p.91 / Chapter §9.6 --- The Use of Human Intelligence --- p.92 / APPENDICES / Chapter A.1 --- K-MEANS / Chapter A.2 --- Unsupervised Learning Approach / Chapter A.3 --- Other Algorithms (Maximum Distance & ISODATA) / Chapter A.4 --- Possible Improvement / Chapter A.5 --- Theories on Statistical Discriminant Analysis / Chapter A.6 --- Passage used in Testing the Performance of the Classifier with Context Consideration / Chapter A.7 --- A Partial List of Semantically Related Chinese Characters / Chapter A.8 --- An Example of Misclassification Table / Chapter A.9 --- "Listing of the Program ""CHDIS.C""" / REFERENCE

Typescript (photocopy). / Digitized by Kansas Correctional Industries

by Yen-Hui Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 76-78). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background ® / Chapter 2.1 --- Multilingual Information Processing --- p.6 / Chapter 2.1.1 --- Codesets --- p.6 / Chapter 2.1.2 --- Converters --- p.8 / Chapter 2.1.3 --- Collations --- p.8 / Chapter 2.1.4 --- Internationalization and Localization --- p.9 / Chapter 2.2 --- OBST --- p.10 / Chapter 2.3 --- Related Work --- p.11 / Chapter 2.3.1 --- Sybase --- p.11 / Chapter 2.3.2 --- Oracle --- p.13 / Chapter 2.3.3 --- COBASE --- p.15 / Chapter 2.3.4 --- SQL92 Standard --- p.15 / Chapter 3 --- Defects of Existing Systems and Proposed Language Extensions --- p.21 / Chapter 3.1 --- Defects of Existing Methods - Locale Model --- p.22 / Chapter 3.2 --- Defects of SQL92 --- p.22 / Chapter 3.3 --- Proposed Language Extensions --- p.24 / Chapter 3.3.1 --- Inserting tuples --- p.24 / Chapter 3.3.2 --- Updating tuples --- p.25 / Chapter 3.3.3 --- Querying (Retrieving tuples) --- p.26 / Chapter 3.3.4 --- String matching --- p.26 / Chapter 3.3.5 --- Performing Joins --- p.28 / Chapter 3.3.6 --- Sorting and Indexing --- p.30 / Chapter 4 --- DBMS Design and Implementation --- p.32 / Chapter 4.1 --- System Architecture --- p.34 / Chapter 4.2 --- Lexical Analyzer and Parser --- p.36 / Chapter 4.3 --- Database Objects --- p.37 / Chapter 4.4 --- Data Dictionary --- p.39 / Chapter 4.5 --- Character Objects Related Facilities --- p.41 / Chapter 4.5.1 --- Codesets --- p.42 / Chapter 4.5.2 --- Collations --- p.46 / Chapter 4.5.3 --- Converters --- p.48 / Chapter 4.6 --- Indexing --- p.50 / Chapter 4.7 --- Query Processor --- p.52 / Chapter 4.7.1 --- Join --- p.54 / Chapter 4.7.2 --- Sorting tuples - the order by clause --- p.54 / Chapter 4.7.3 --- "Group Structure, Aggregate Functions and Projection" --- p.55 / Chapter 4.8 --- Scalar and Conditional Expressions --- p.56 / Chapter 4.8.1 --- Representation of Scalar and Condition Expressions --- p.57 / Chapter 4.8.2 --- Implementations --- p.58 / Chapter 4.9 --- User Interface --- p.59 / Chapter 5 --- Case Study - A Bookshop --- p.62 / Chapter 5.1 --- Creating tables and inserting tuples --- p.62 / Chapter 5.2 --- Updating Tuples --- p.65 / Chapter 5.3 --- Querying --- p.65 / Chapter 5.4 --- String Matching --- p.66 / Chapter 5.5 --- Performing Joins --- p.67 / Chapter 5.6 --- Ordering Display --- p.68 / Chapter 5.7 --- Indexing --- p.70 / Chapter 6 --- Conclusions and Future Work --- p.73 / Bibliography --- p.76 / Chapter A --- Grammer Rules --- p.79 / Chapter A.1 --- Data Definition Language --- p.79 / Chapter A.2 --- Data Manipulation Language --- p.82 / Chapter A.3 --- Condition Expressions --- p.83 / Chapter A.4 --- Scalar Expressions --- p.84 / Chapter A.5 --- Data Type --- p.85 / Chapter A.6 --- Names and Identifiers --- p.87 / Chapter A.7 --- Lexical Element --- p.88 / Chapter B --- Programmers' Guide --- p.90 / Chapter B.l --- Charset.obst --- p.92 / Chapter B.2 --- Table.obst --- p.95 / Chapter B.3 --- dbSchema.obst --- p.101 / Chapter B.4 --- dbEnv.obst --- p.103 / Chapter B.5 --- Query.obst --- p.104 / Chapter B.6 --- Misc.obst and External.obst --- p.117 / Chapter B.7 --- Main --- p.118 / Chapter B.8 --- RPC interfaces --- p.120 / Chapter C --- Installation Manual --- p.122 / Chapter C.l --- Steps to install the DBMS server --- p.123 / Chapter C.2 --- Steps to install the WWW client --- p.124 / Chapter D --- User Manual --- p.125 / Chapter D.l --- User Interface Layout --- p.125 / Chapter D.2 --- Steps in Performing Database Operations --- p.128 / Chapter D.2.1 --- Creating new tables --- p.129 / Chapter D.2.2 --- Browsing existing tables --- p.131 / Chapter D.2.3 --- Inserting new records --- p.131 / Chapter D.2.4 --- Deleting records --- p.132 / Chapter D.2.5 --- Creating indexes --- p.132 / Chapter D.2.6 --- Showing existing schemas and tables --- p.133