16.3 Data Warehouse Specification

This section deals with the data warehouse specification. An XML data warehouse is defined as a set of materialized views. In the first subsection we present our view model for XML documents. Next, we present the graphic tool that enables the data warehouse designer to specify the XML views.

16.3.1 View Model for XML Documents

Since the data warehouse is defined as a set of views, the main issue of data warehouse definition is the view model. We present briefly in this section the main characteristics of our view model. This model has been presented in detail in work by X. Baril and Z. Bellahsène (Baril and Bellahsène 2000).

Our view model fulfills the following requirements:

• Closure property: A view defined on XML document(s) should yield an XML document as output. This allows us to transparently use a view or a document. From the data warehouse point of view, this property implies that the unified view of sources is an XML document.

• Restructuring possibilities: The view mechanism enables restructuring elements of the source(s) document(s). We can distinguish two classes of views: (1) select views that extract existing documents from sources, and (2) composite views that create new elements or attributes. For this latter class, new elements of the result may be created from several source elements. Furthermore, aggregation functions (i.e., sum, avg, min, max, count, etc.) can be used to define new values. Moreover, sorting and grouping elements is also provided.

• DTD inference: The view result should be associated to a DTD. This DTD is inferred from the view definition and possibly from source DTDs if they exist. The inferred DTD can be used to optimize the view storage or to query the view. From the data warehouse point of view, the inferred DTD is used to give a global integrated schema on which user queries can be formulated.

Each view is composed of a result pattern that specifies the structure of the result. This result pattern uses variables that are defined in fragments. A fragment is a collection of patterns: Each pattern uses variables to define data to match in a source. A fragment is composed of several patterns defining the same variables on different sources and provides the union of their data.

For example, let us consider a source "senior.xml" containing information about senior researchers and a source "senior.xml" containing information about Ph.D. students. To define a fragment "f1" containing the names and birthdays of senior researchers and Ph.D. students, we would define two patterns: one pattern matching names and birthdays of the senior researchers on source "senior.xml" and another one matching names and birthdays of the Ph.D. students on source "student.xml".

To define composite views, the result pattern can be based on several fragments. For this purpose, fragments are linked using join conditions. A join condition involves two variables defined in two different fragments.

Listing 16.1 shows a complete example of a view specification involving two fragments. Let us consider a view retrieving for each author their name, surname, and a list of the titles of their publications. The view is composed of a result pattern, two fragments, and a join condition. The fragment "f3" contains a pattern that matches the "author" elements, while "f4" contains a pattern that matches the "inproceedings" elements, with their "title" attribute and "authorlink" subelements. These subelements contain a "ref" attribute that references the author of the publication. The join element gives the join condition between the two fragments "f3" and "f4". The result element contains the result pattern. Each item of the view result is an "author" element, containing a "name" attribute (having the value of the "name" variable) and a "title" subelement (having the value of the "title" variable). The group-by element indicates that the result is grouped by "name" values (i.e., for an author there are possibly several "title" subelements). The part of the DTD validating this specification is presented in section 16.4.2, "View Definition," later in this chapter.

Listing 16.1 Example of View Definition

<view id="authorspublications">
<result>
 <result.node name="author" type="element">
  <result.node name="name" type="attribute">
   <result.node name="name" type="variable"></result.node>
  </result.node>
  <result.node name="title" type="element">
   <result.node name="title" type="variable"></result.node>
  </result.node>
 </result.node>
 <groupby name="name"></groupby>
</result>
<fragment id="f3">
 <pattern id="p3" source="1">
  <pattern.node name="author" type="element">
   <pattern.node name="id" type="attribute"
                 bind="id"></pattern.node>
   <pattern.node name="name" type="attribute"
                 bind="name"></pattern.node>
   <pattern.node name="surname" type="attribute"
                 bind="surname"></pattern.node>
  </pattern.node>
 </pattern>
</fragment>
<fragment id="f4">
 <pattern id="p4" source="1">
  <pattern.node name="inproceedings" type="element">
   <pattern.node name="title" type="attribute" bind="title"></pattern.node>
   <pattern.node name="authorlink" type="element">
    <pattern.node name="ref" type="attribute" bind="ref"></pattern.node>
   </pattern.node>
  </pattern.node>
 </pattern>
</fragment>
<join leftfragment="f3" leftvariable="id"
      rightfragment="f4" rightvariable="ref">
</join>
</view>

16.3.2 Graphic Tool for Data Warehouse Specification

We propose a graphic tool to help the user in the specification of the data warehouse. The editor allows us to create this specification without knowledge of the exact structure of the warehouse definition. We have proposed (in Baril and Bellahsène 2001) helpers for view definitions that we plan to integrate with DAWAX. These helpers allow us to define patterns without knowledge of the source structure. They use the DTD (if available) and the dataguide to propose choices for the pattern specification.

Figure 16.2 shows the graphic editor for the data warehouse specification. The XML document defining the data warehouse is represented as a tree. New elements (sources, views, fragments, etc.) can be added by way of a contextual popup menu. The popup menu suggests possible choices for adding or updating the current element. In the example, the popup menu for a view element suggests the addition of a fragment or a join, and the deletion of a view.

The fragment "f4" of the view given as an example is displayed in Figure 16.2. It contains a pattern ("p4") with an identifier and a source attribute. The root pattern node of the pattern is displayed, and due to space limitations, its child nodes are not expanded in the tree.

Top Title TOC Preface P.1 What Is XML? P.2 XML Concepts P.3 XML-Related Technologies P.4 XML Data Management P.5 How This Book Is Organized P.6 Who Should Read This Book P.7 Resources Acknowledgments Chapter 8 Chapter 12 Chapter 13 Chapter 14 Chapter 19 Part I: What Is XML? Chapter 1. Information Modeling with XML 1.1 Introduction 1.2 XML as an Information Domain 1.3 How XML Expresses Information 1.4 Patterns in XML 1.5 Common XML Information-Modeling Pitfalls 1.6 A Very Simple Way to Design XML 1.7 Conclusion Part II: Native XML Databases Chapter 2. Tamino-Software AG's Native XML Server 2.1 Introduction 2.2 Tamino Architecture and APIs 2.3 XML Storage 2.4 Querying XML 2.5 Tools 2.6 Full Database Functionality 2.7 Conclusion Chapter 3. eXist Native XML Database 3.1 Introduction 3.2 Features 3.3 System Architecture Overview 3.4 Getting Started 3.5 Query Language Extensions 3.6 Application Development 3.7 Technical Background 3.8 Conclusion Chapter 4. Embedded XML Databases 4.1 Introduction 4.2 A Primer on Embedded Databases 4.3 Embedded XML Databases 4.4 Building Applications for Embedded XML Databases 4.5 Conclusion Part III: XML and Relational Databases Chapter 5. IBM XML-Enabled Data Management Product Architecture & Technology 5.1 Introduction 5.2 Product and Technology Offering Summaries 5.3 Current Architecture and Technology 5.4 Future Architecture and Technology 5.5 Conclusion Notices Chapter 6. Supporting XML in Oracle9i 6.1 Introduction 6.2 Storing XML as CLOB 6.3 XMLType 6.4 Using XSU for Fine-Grained Storage 6.5 Building XML Documents from Relational Data 6.6 Web Access to the Database 6.7 Special Oracle Features 6.8 Conclusion Chapter 7. XML Support in Microsoft SQL Server 2000 7.1 Introduction 7.2 XML and Relational Data 7.3 XML Access to SQL Server 7.4 Serializing SQL Query Results into XML 7.5 Providing Relational Views over XML 7.6 SQLXML Templates 7.7 Providing XML Views over Relational Data 7.8 Conclusion Chapter 8. A Generic Architecture for Storing XML Documents in a Relational Database 8.1 Introduction 8.2 System Architecture 8.3 The Data Model 8.4 Creating the Database 8.5 Connecting to the Repository 8.6 Uploading XML Documents 8.7 Querying the Repository 8.8 Further Enhancements 8.9 Conclusion Chapter 9. An Object-Relational Approach to Building a High-Performance XML Repository 9.1 Introduction 9.2 Overview of XML Use-Case Scenario 9.3 High-Level System Architecture 9.4 Detailed Design Descriptions 9.5 Conclusion Part IV: Applications of XML Chapter 10. Knowledge Management in Bioinformatics 10.1 Introduction 10.2 A Brief Molecular Biology Background 10.3 Life Sciences Are Turning to XML to Model Their Information 10.4 A Genetic Information Model 10.5 NeoCore XMS* 10.6 Integration of BLAST into NeoCore XMS Conclusion Chapter 11. Case Studies of XML Used with IBM DB2 Universal Database 11.1 Introduction 11.2 Case Study 1: "Our Most Valued Customers Come First" 11.3 Case Study 2: "Improve Cash Flow" 11.4 Conclusion Notices Chapter 12. The Design and Implementation of an Engineering Data Management System Using XML and J2EE 12.1 Introduction 12.2 Background and Requirements 12.3 Overview 12.4 Design Choices 12.5 Future Directions 12.6 Conclusion Chapter 13. Geographical Data Interchange Using XML-Enabled Technology within the GIDB System 13.1 Introduction 13.2 GIDB METOC Data Integration 13.3 GIDB Web Map Service Implementation 13.4 GIDB GML Import and Export 13.5 Conclusion Chapter 14. Space Wide Web by Adapters in Distributed Systems Configuration from Reusable Components 14.1 Introduction 14.2 Advanced Concept Description: The Research Problem 14.3 Integration of Components with Architecture 14.4 Example 14.5 Future Generation NASA Institute for Advanced Concepts, Space Wide Web Research, and Boundaries 14.6 Advanced Concept Development 14.7 Conclusion Chapter 15. XML as a Unifying Framework for Inductive Databases 15.1 Introduction 15.2 Past Work 15.3 The Proposed Data Model: XDM 15.4 Benefits of XDM 15.5 Toward Flexible and Open Systems 15.6 Related Work 15.7 Conclusion Chapter 16. Designing and Managing an XML Warehouse 16.1 Introduction 16.2 Architecture 16.3 Data Warehouse Specification 16.4 Managing the Metadata 16.5 Storage and Management of the Data Warehouse 16.6 DAWAX: A Graphic Tool for the Specification and Management of a Data Warehouse 16.7 Related Work 16.8 Conclusion Part V: Performance and Benchmarks Chapter 17. XML Management System Benchmarks 17.1 Introduction 17.2 Benchmark Specification 17.3 Benchmark Data Set 17.4 Existing Benchmarks for XML 17.5 Conclusion Chapter 18. The Michigan Benchmark: A Micro-Benchmark for XML Query Performance Diagnostics 18.1 Introduction 18.2 Related Work 18.3 Benchmark Data Set 18.4 Benchmark Queries 18.5 Using the Benchmark 18.6 Conclusion Chapter 19. A Comparison of Database Approaches for Storing XML Documents 19.1 Introduction 19.2 Data Models for XML Documents 19.3 Databases for Storing XML Documents 19.4 Benchmarking Specification 19.5 Test Results 19.6 Related Work 19.7 Summary Chapter 20. Performance Analysis between an XML-Enabled Database and a Native XML Database 20.1 Introduction 20.2 Related Work 20.3 Methodology 20.4 Database Design 20.5 Discussion 20.6 Experiment Result 20.7 Conclusion Chapter 21. Conclusion References Contributors Editors Chapter 1: Information Modeling with XML Chapter 2: Tamino-Software AG's Native XML Server Chapter 3: eXist Native XML Database Chapter 4: Embedded XML Databases Chapter 5: IBM XML-Enabled Data Management Product Architecture and Technology Chapter 6: Supporting XML in Oracle9i Chapter 7: XML Support in Microsoft SQL Server 2000 Chapter 8: A Generic Architecture for Storing XML Documents in a Relational Database Chapter 9: An Object-Relational Approach to Building a High-Performance XML Repository Chapter 10: Knowledge Management in Bioinformatics Chapter 11: Case Studies of XML Used with IBM DB2 Universal Database Chapter 12: The Design and Implementation of an Engineering Data Management System Using XML and J2EE Chapter 13: Geographical Data Interchange Using XML-Enabled Technology within the GIDB System Chapter 14: Space Wide Web by Adapters in Distributed Systems Configuration from Reusable Components Chapter 15: XML as a Unifying Framework for Inductive Databases Chapter 16: Designing and Managing an XML Warehouse Chapter 17: XML Management System Benchmarks Chapter 18: The Michigan Benchmark: A Micro-Benchmark for XML Query Performance Diagnostics Chapter 19: A Comparison of Database Approaches for Storing XML Documents Chapter 20: Performance Analysis between an XML-Enabled Database and a Native XML Database Remember the name: eTutorials.org Copyright eTutorials.org 2008-2023. 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