臺灣博碩士論文加值系統

傳統的關聯式交易資料庫在應付更大量資料的存取或查詢時已逐漸不敷使用，因而有了資料倉儲觀念的興起。資料倉儲透過交易資料之合併、轉換與整合，以提昇企業決策者之生產力及決策品質，並支援線上分析處理(OLAP)。然而，資料倉儲設計為一複雜且知識密集之過程。為了有效地支援資料倉儲設計過程，本研究的目的在於發展以資料探勘(Data Mining)技術為基礎之資料倉儲設計支援系統。更明確地說，本研究將著重於以一般化星狀綱目(Star Schemas)之知識型態來支援資料倉儲設計。本研究發展上述知識型態之學習與推理機制，同時也針對所提出的技術進行實證評估。實證評估結果顯示研究成果能有效地提昇資料倉儲設計之品質。

Traditional relational database model does not have enough capability to cope with a great deal of data in finite time. To address these requirements, data warehouses and online analytical processing (OLAP) have emerged. Data warehouses improve the productivity of corporate decision makers through consolidation, conversion, transformation, and integration of operational data, and supports online analytical processing (OLAP). The data warehouse design is a complex and knowledge intensive process. It needs to consider not only the structure of the underlying operational databases (source-driven), but also the information requirements of decision makers (user-driven). Past research focused predominately on supporting the source-driven data warehouse design process, but paid less attention to supporting the user-driven data warehouse design process. Thus, the goal of this research is to propose a user-driven data warehouse design support system based on the knowledge discovery approach. Specifically, a Data Warehouse Design Support System was proposed and the generalization hierarchy and generalized star schemas were used as the data warehouse design knowledge. The technique for learning these design knowledge and reasoning upon them were developed. An empirical evaluation study was conducted to validate the effectiveness on the proposed techniques in supporting data warehouse design process. The result of empirical evaluation showed that this technique was useful to support data warehouse design especially on reducing the missing design and enhancing the potentially useful design.

CHAPTER 1. INTRODUCTION............................................1
1.1 Background.....................................................1
1.2 Research Motivation and Objective..............................3
1.3 Organization of the Thesis.....................................4

CHAPTER 2. LITERATURE REVIEW.......................................5
2.1 Definition of Star Schema......................................5
2.1.1 Fact Table...................................................5
2.1.2 Measure......................................................6
2.1.3 Dimension Table..............................................7
2.2 Commonsense Knowledge-based Approach to Database Design........7

CHAPTER 3. ARCHITECTURE OF DATA WAREHOUSE DESIGN SUPPORT SYSTEM...14

CHAPTER 4. LEARNING IN DATA WAREHOUSE DESIGN SUPPORT SYSTEM.......18
4.1 Distance Function.............................................18
4.1.1 Distance of Measures........................................18
4.1.2 Distance of Dimension Tables................................21
4.1.3 Distance of Star Schemas....................................22
4.2 Generation of Generalized Star Schema.........................23
4.3 Learning: Agglomerative Clustering as Generalization Process..24

CHAPTER 5. REASONING IN DATA WAREHOUSE DESIGN SUPPORT SYSTEM......30
5.1 Reasoning Process.............................................30
5.2 Reasoning Algorithm...........................................32

CHAPTER 6. EMPIRICAL EVALUATION...................................36
6.1 Implementation Environment....................................36
6.2 Case Collection for Learning..................................36
6.3 Parameter Tuning Experiments..................................38
6.3.1 Parameter Tuning for Distance Function......................38
6.3.2 Parameter Tuning for Extension Ratio in Learning Algorithm..41
6.4 Empirical Evaluation..........................................45
6.4.1 Participant Profile.........................................47
6.4.2 Cases for Experiment........................................47
6.4.3 Grading Criteria............................................48
6.4.4 Data Analysis and Results...................................49

CHAPTER 7. CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS.............60

REFERENCES........................................................62

APPENDIX A: DESCRIPTION OF EXPERIMENT TASK........................64

APPENDIX B: DEFINITIONS OF GRADING CRITERIA.......................70

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