Preface. Acknowledgments. PART I: INTRODUCTION TO DATA MODELING. 1. Data Modeling: An Overview. Chapter Objectives. Data Model Defined. What is a Data Model? Why Data Modeling? Who Performs Data Modeling? Information Levels. Classification of Information Levels. Data Models at Information Levels. Conceptual Data Modeling. Data Model Components. Data Modeling Steps. Data Model Quality. Significance of Data Model Quality. Data Model Characteristics. Ensuring Data Model Quality. Data System Development. Data System Development Life Cycle (DDLC). Roles and Responsibilities. Modeling the Information Requirements. Applying Agile Modeling Principles. Data Modeling Approaches and Trends. Data Modeling Approaches. Modeling for Data Warehouse. Other Modeling Trends. Chapter Summary. Review Questions. 2. Methods, Techniques, and Symbols. Chapter Objectives. Data Modeling Approaches. Semantic Modeling. Relational Modeling. Entity-Relationship Modeling. Binary Modeling. Methods and Techniques. Peter Chen (E-R) Modeling. Information Engineering. IDEF1X. Richard Barker's. ORM (Object Role Modeling). XML (eXtensible Markup Language). Summary and Comments. Unified Modeling Language (UML). Data Modeling Using UML. UML in the Development Process. Chapter Summary. Review Questions. PART II. DATA MODELING FUNDAMENTALS. 3. Anatomy of a Data Model. Chapter Objectives. Data Model Composition. Models at Different Levels. Conceptual Model: Review Procedure. Conceptual Model: Identifying Components. Case Study. Description. E-R Model. UML Model. Creation of Models. User Views. View Integration. Entity Types. Specialization/Generalization. Relationships. Attributes. Identifiers. Review of the Model Diagram. Logical Model: Overview. Model Components. Transformation Steps. Relational Model. Physical Model: Overview. Model Components. Transformation Steps. Chapter Summary. Review Questions. 4. Objects or Entities in Detail. Chapter Objectives. Entity Types or Object Sets. Comprehensive Definition. Identifying Entity Types. Homonyms and Synonyms. Category of Entity Types. Exploring Dependencies. Dependent or Weak Entity Types. Classifying Dependencies. Representation in the Model. Generalization and Specialization. Why Generalize or Specialize? Super-types and Sub-types. Generalization Hierarchy. Inheritance of Attributes. Inheritance of Relationships. Constraints. Rules Summarized. Special Cases and Exceptions. Recursive Structures. Conceptual and Physical. Assembly Structures. Entity Type Vs Attribute. Entity Type Vs Relationship. Modeling Time Dimension. Categorization. Entity Validation Checklist. Completeness. Correctness. Chapter Summary. Review Questions. 5. Attributes and Identifiers in Detail. Chapter Objectives. Attributes. Properties or Characteristics. Attributes as Data. Attribute Values. Names and Descriptions. Attribute Domains. Definition of a Domain. Domain Information. Attribute Values and Domains. Split Domains. Misrepresented Domains. Resolution of Mixed Domains. Constraints for Attributes. Value Set. Range. Type. Null Values. Types of Attributes. Single-Valued and Multi-Valued Attributes. Simple and Composite Attributes. Attributes with Stored and Derived Values . Optional Attributes. Identifiers or Keys. Need for Identifiers. Definitions of Keys. Guidelines for Identifiers. Key in Generalization Hierarchy. Attribute Validation Checklist. Completeness. Correctness. Chapter Summary. Review Questions. 6. Relationships in Detail. Chapter Objectives. Relationships. Associations. Relationship?Two-sided. Relationship Sets. Double Relationships. Relationship Attributes. Degree of Relationships. Unary Relationship. Binary Relationship. Ternary Relationship. Quaternary Relationship. Structural Constraints. Cardinality Constraint. Participation Constraint. Dependencies. Entity Existence. Relationship Types. Identifying Relationship . Non-identifying Relationship. Maximum and Minimum Cardinalities. Mandatory Conditions - Both Ends. Optional Condition - One End. Optional Condition - Other End. Optional Conditions - Both Ends. Special Cases. Gerund. Aggregation. Access Pathways. Design Issues. Relationship Or Entity Type? Ternary Relationship Or Aggregation? Binary Or N-ary Relationship? One-to-One Relationships. One-to-Many Relationships. Circular Structures. Redundant Relationships. Multiple Relationships. Relationship Validation Checklist. Completeness. Correctness. Chapter Summary. Review Questions. PART III. DATA MODEL IMPLEMENTATION. 7. Data Modeling to Database Design. Chapter Objectives. Relational Model: Fundamentals. Basic Concepts. Structure and Components. Data Integrity Constraints. Transition to Database Design. Design Approaches. Conceptual to Relational Model. Traditional Method. Evaluation of Design Methods. Model Transformation Method. The Approach. Mapping of Components. Entity Types to Relations. Attributes to Columns. Identifiers to Keys. Transformation of Relationships. Transformation Summary . Chapter Summary. Review Questions. 8. Data Normalization. Chapter Objectives. Informal Design. Forming Relations from Requirements. Potential Problems. Update Anomaly. Deletion Anomaly. Addition Anomaly. Normalization Methodology. Strengths of the Method. Application of the Method. Normalization Steps. Fundamental Normal Forms. First Normal Form. Second Normal Form. Third Normal Form. Boyce-Codd Normal Form. Higher Normal Forms. Fourth Normal Form. Fifth Normal Form. Domain-Key Normal Form. Normalization Summary. Review of the Steps. Normalization as Verification. Chapter Summary. Review Questions. 9. Modeling for Decision-Support Systems. Chapter Objectives. Decision-Support Systems. Need for Strategic Information. History of Decision-Support Systems. Operational Vs Informational Systems. System Types and Modeling Methods. Data Warehouse. Data Warehouse Defined. Major Components. Data Warehousing Applications. Modeling: Special Requirements. Dimensional Modeling. Dimensional Modeling Basics. STAR Schema. Snowflake Schema. Families of STARS. Transition to Logical Model. OLAP Systems. Features and Functions of OLAP. Dimensional Analysis. Hypercubes. OLAP Implementation Approaches. Data Modeling for OLAP. Data Mining Systems. Basic Concepts. Data Mining Techniques. Data Preparation and Modeling. Data Preprocessing. Data Modeling. Chapter Summary. Review Questions. PART IV. PRACTICAL APPROACH TO DATA MODELING. 10. Ensuring Quality in the Data Model. Chapter Objectives. Significant of Quality. Why Emphasize Quality? Good and Bad Models. Approach to Good Modeling. Quality of Definitions. Importance of Definitions. Aspects of Quality Definitions. Correctness. Completeness. Clearness. Format. Checklists. High-Quality Data Model. Meaning of Data Model Quality. Quality Dimensions. What is a High-Quality Model? Benefits of High-Quality Models. Quality Assurance Process. Aspects of Quality Assurance. Stages of Quality Assurance Process. Data Model Review. Data Model Assessment. Chapter Summary. Review Questions. 11. Agile Data Modeling in Practice. Chapter Objectives. The Agile Movement. How It Got Started. Principles of Agile Development. Philosophies. Generalizing Specialists. Agile Modeling. What is Agile Modeling? Basic Principles. Auxiliary Principles. Practicing Agile Modeling. Primary Practices. Additional Practices. Role of Agile DBA. Agile Documentation. Recognizing an Agile Model. Feasibility. Evolutionary Data Modeling. Traditional Approach. Need for Flexibility. Nature of Evolutionary Modeling. Benefits. Chapter Summary. Review Questions. 12. Data Modeling: Practical Tips. Chapter Objectives. Tips and Suggestions. Nature of Tips. How Specified. How to Use Them. Requirements Definition. Interviews. Group Sesssions. Geographically Dispersed Groups. Documentation. Change Management. Notes for Modeling. Stakeholder Participation. Organizing Participation. User Liaison. Continuous Interaction. Multiple Sites. Iterative Modeling. Establishing Cycles. Determining Increments. Requirements--Model Interface. Integration of Partial Models. Special Cases. Legal Entities. Locations and Places. Time Periods. Persons. Bill-of-Materials. Conceptual Model Layout. Readability and Usability. Component Arrangement. Adding Texts. Visual Highlights. Logical Data Model. Enhancement Motivation. Easier DB Implementation. Performance Improvement. Storage Management. Enhanced Representation. Chapter Summary. Review Questions. References. Glossary. Index.
CUTTING-EDGE CONTENT AND GUIDANCE FROM A DATA WAREHOUSING EXPERT—NOW EXPANDED TO REFLECT FIELD TRENDS Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field. The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery. This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information. Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.
INTRODUCTION UNDERSTANDING THE ENVIRONMENT Documentation Data Source Platforms SAN, Failover, and RAID technologiesData Sources-primary and secondary Data Ownership Data structures Data Usage Global Enterprise versus Entrepreneurial Multi media sources Built or leased services, equipment, and telecom infrastructure MAINTAINING THE DATA Data Change Processes Database normalization/standards Backups Upgrades Outsourcing programming and DBA activities Changing to a relational database Data warehousing Cost of change Keeping technical resources available for maintaining systems Cost of ownership for technology The effect of the emerging technology vendor's distribution model DATA ACCESS Internal/External Access Customer Relationship Management Financial Information Fat Client versus Thin Client-impact of the current trends Software licensing Reporting Data subsets Search engines SAFEKEEPING A PRIMARY ENTERPRISE RESOURCE Security Encryption E-mails Firewalls Viruses inoculation Web Sources Data Integrity Long term data storage Disaster Recovery
The mortality among retail stores is disturbing to say the least. And when we realize that two thirds of the misfortunes are attributed to incompetency and ignorance of the retail business principles, we appreciate the need for a stimulation of constructive thought and, indeed, often of energy. There are several excellent books on retailing already on the market. This book is not offered as something better but as something different and something more directly applicable to the small store needs.
In many cases their mss. Are accompanied by long confidential letters, appeals to one's feelings, attacks on one's sympathy. Now and then I detect something of merit in an amateur article; but too often the merit lies in the evident disadvantages of the circumstances under which the paper has been written. Misled on this tack I return a civil reply and say, Try again; you may succeed. The writer tries again. He does not succeed. I say so. His ms. Goes back. Then I have been unkind; I have raised hopes only to blight them. Sometimes the ms. Is lost or mislaid, the writer having omitted to put his name or address upon it. Then it cannot be returned and the young author pours out his wrath wildly upon the editor. I sympathise with him, despite the suffering he causes; but I tell him now, as I have told him before, that if he would retain his literary treasures, he must keep copies of them. This is easily done; the manifold letter writer and the copying press are old institutions.
In civil affairs, the year of the king's reign seems to have been the general date even in common deeds, till after the Restoration. During Cromwell's usurpation the year of our Lord was introduced, because they did not choose to date by the years of the king's reign; and this was after wards continued for convenience.
his book should satisfy those who want a different perspective than the official Oracle documentation. It will cover all important aspects of a data warehouse while giving the necessary examples to make the reading a lively experience.聰 - Tim Donar, Author and Systems Architect for Enterprise Data WarehousesTuning a data warehouse database focuses on large transactions, mostly requiring what is known as throughput. Throughput is the passing of large amounts of information through a server, network and Internet environment, backwards and forwards, constantly! The ultimate objective of a data warehouse is the production of meaningful and useful reporting, from historical and archived data. The trick is to make the reports print within an acceptable time frame.A data model contains tables and relationships between tables. Tuning a data model involves Normalization and Denormalization. Different approaches are required depending on the application, such as OLTP or a Data Warehouse. Inappropriate database design can make SQL code impossible to tune. Poor data modeling can have a most profound effect on database performance since all SQL code is constructed from the data model.* Takes users beyond basics to critical issues in running most efficient data warehouse applications* Illustrates how to keep data going in and out in the most productive way possible* Focus is placed on Data Warehouse performance tuning
The Profit Impact of Business Intelligence presents an A-to-Z approach for getting the most business intelligence (BI) from a company's data assets or data warehouse. BI is not just a technology or methodology, it is a powerful new management approach that when done right can deliver knowledge, efficiency, better decisions, and profit to almost any organization that uses it. When BI first came on the scene, it promised a lot but often failed to deliver. The missing element was the business-centric focus explained in this book. It shows how you can achieve the promise of BI by connecting it to your organization strategic goals, culture, and strengths while correcting your BI weaknesses. It provides a practical, process-oriented guide to achieve the full promise of BI; shows how world-class companies used BI to become leaders in their industries; helps senior business and IT executives understand the strategic impact of BI and how they can ensure a strong payoff from their BI investments; and identifies the most common mistakes organizations make in implementing BI. The book also includes a helpful glossary of BI terms; a BI readiness assessment for your organization; and Web links and extensive references for more information.A practical, process-oriented book that will help organizations realize the promise of BIWritten by Nancy and Steve Williams, veteran consultants and instructors with hands-on, "in the trenches" experience in government and corporate business intelligence applicationsWill help senior business and IT executives understand the strategic impact of BI and how they can help ensure a strong payoff on BI investments
You go online to buy a digital camera. Soon, you realize you've bought a more expensive camera than intended, along with extra batteries, charger, and graphics software-all at the prompting of the retailer.Happy with your purchases? The retailer certainly is, and if you are too, you both can be said to be the beneficiaries of "customer intimacy" achieved through the transformation of data collected during this visit or stored from previous visits into real business intelligence that can be exercised in real time.Data Warehousing and Business Intelligence for e-Commerce is a practical exploration of the technological innovations through which traditional data warehousing is brought to bear on this and other less modest e-commerce applications, such as those at work in B2B, G2C, B2G, and B2E models. The authors examine the core technologies and commercial products in use today, providing a nuts-and-bolts understanding of how you can deploy customer and product data in ways that meet the unique requirements of the online marketplace-particularly if you are part of a brick-and-mortar company with specific online aspirations. In so doing, they build a powerful case for investment in and aggressive development of these approaches, which are likely to separate winners from losers as e-commerce grows and matures.*
At 70 terabytes and growing, Wal-Mart's data warehouse is still the world's largest, most ambitious, and arguably most successful commercial database. Written by one of the key figures in its design and construction, Data Warehousing: Using the Wal-Mart Model gives you an insider's view of this enormous project. Continuously drawing from this example, the author teaches you the general principles and specific techniques you need to understand to be a valuable part of your organization's own data warehouse project, however large or small. You'll emerge with a practical understanding of both the business and technical aspects of building a data warehouse for storing and accessing data in a strategically useful way. What further sets this book apart is its focus on the informational needs of retail companies-including both market and organizational issues that affect the data's collection and use. If retail is your field, this book will prove especially valuable as you develop and implement your company's ideal data warehouse solution. * Written by a member of the team of four engineers who designed and built the Wal-Mart Data Warehouse database, a team whose database design was recognized internally in 1991 by Wal-Mart with the company's Team Innovational Technical award. * Provides essential information for project managers, consultants, data warehouse managers, and data architects. * Takes an in-depth look at a wide range of technical issues, including architecture, construction approaches, tool selection, database system selection, and maintenance. * Addresses issues specific to retail business: vendors, inventory, sales analysis, geography, article categories, and more. * Explains how to determine business requirements at the outset of the project-and how to develop return on investment analyses after the warehouse has been brought online.
- 1
- 2
- 3
- 4
- 5
- 6
- 7
隐私声明 | 电子书 | 出版社
主办单位:中国教育图书进出口有限公司
京ICP备11007853号-12 
京公网安备 11010802027623号
Copyright ©爱学术平台 2021, All Rights Reserved