Complex Type Structures for Objects and Literals
11.1.3 Complex Type Structures for Objects and Literals
Another feature of an ODMS (and ODBs in general) is that objects and literals may have a type structure of arbitrary complexity in order to contain all of the necessary information that describes the object or literal. In contrast, in traditional database systems, information about a complex object is often scattered over many relations or records, leading to loss of direct correspondence between a real-world object and its database representation. In ODBs, a complex type may be constructed from other types by nesting of type constructors. The three most basic constructors are atom, struct (or tuple), and collection.
1. One type constructor has been called the atom constructor, although this term is not used in the latest object standard. This includes the basic built-in
data types of the object model, which are similar to the basic types in many programming languages: integers, strings, floating point numbers, enumer- ated types, Booleans, and so on. They are called single-valued or atomic types, since each value of the type is considered an atomic (indivisible) sin- gle value.
2. A second type constructor is referred to as the struct (or tuple) constructor. This can create standard structured types, such as the tuples (record types)
in the basic relational model. A structured type is made up of several compo- nents, and is also sometimes referred to as a compound or composite type. More accurately, the struct constructor is not considered to be a type, but rather a type generator, because many different structured types can be cre- ated. For example, two different structured types that can be created are:
11.1 Overview of Object Database Concepts 359
struct CollegeDegree<Major: string, Degree: string, Year: date>. To create complex nested type structures in the object model, the collection type con- structors are needed, which we discuss next. Notice that the type construc- tors atom and struct are the only ones available in the original (basic) relational model.
3. Collection (or multivalued) type constructors include the set(T), list(T), bag(T) , array(T), and dictionary(K,T) type constructors. These allow part of an object or literal value to include a collection of other objects or values when needed. These constructors are also considered to be type generators because many different types can be created. For example, set(string), set(integer), and set(Employee) are three different types that can be created
from the set type constructor. All the elements in a particular collection value must be of the same type. For example, all values in a collection of type set(string) must be string values.
The atom constructor is used to represent all basic atomic values, such as integers, real numbers, character strings, Booleans, and any other basic data types that the system supports directly. The tuple constructor can create structured values and
objects of the form <a 1 :i 1 ,a 2 :i 2 , ..., a n :i n >, where each a j is an attribute name 10 and each i j is a value or an OID.
The other commonly used constructors are collectively referred to as collection types, but have individual differences among them. The set constructor will create
objects or literals that are a set of distinct elements {i 1 ,i 2 , ..., i n }, all of the same type. The bag constructor (sometimes called a multiset) is similar to a set except that the elements in a bag need not be distinct. The list constructor will create an ordered list
[i 1 ,i 2 , ..., i n ] of OIDs or values of the same type. A list is similar to a bag except that the elements in a list are ordered, and hence we can refer to the first, second, or jth element. The array constructor creates a single-dimensional array of elements of the same type. The main difference between array and list is that a list can have an arbitrary number of elements whereas an array typically has a maximum size. Finally, the dictionary constructor creates a collection of two tuples (K, V), where the value of a key K can be used to retrieve the corresponding value V.
The main characteristic of a collection type is that its objects or values will be a collection of objects or values of the same type that may be unordered (such as a set or
a bag) or ordered (such as a list or an array). The tuple type constructor is often called a structured type, since it corresponds to the struct construct in the C and C++ programming languages.
An object definition language (ODL) 11 that incorporates the preceding type con- structors can be used to define the object types for a particular database application. In Section 11.3 we will describe the standard ODL of ODMG, but first we introduce
10 Also called an instance variable name in OO terminology.
360 Chapter 11 Object and Object-Relational Databases
the concepts gradually in this section using a simpler notation. The type construc- tors can be used to define the data structures for an OO database schema. Figure 11.1 shows how we may declare EMPLOYEE and DEPARTMENT types.
In Figure 11.1, the attributes that refer to other objects—such as Dept of EMPLOYEE or Projects of DEPARTMENT —are basically OIDs that serve as references to other objects to represent relationships among the objects. For example, the attribute Dept of EMPLOYEE is of type DEPARTMENT , and hence is used to refer to a specific DEPARTMENT object (the DEPARTMENT object where the employee works). The value of such an attribute would be an OID for a specific DEPARTMENT object. A binary relationship can be represented in one direction, or it can have an inverse ref- erence. The latter representation makes it easy to traverse the relationship in both directions. For example, in Figure 11.1 the attribute Employees of DEPARTMENT has as its value a set of references (that is, a set of OIDs) to objects of type EMPLOYEE ; these are the employees who work for the DEPARTMENT . The inverse is the reference attribute Dept of EMPLOYEE . We will see in Section 11.3 how the ODMG standard allows inverses to be explicitly declared as relationship attributes to ensure that inverse references are consistent.
Figure 11.1
define type EMPLOYEE
Specifying the object
tuple ( Fname:
string ;
types EMPLOYEE,
Minit:
char ;
DATE, and
Lname:
string ;
DEPARTMENT using
Ssn:
string ;
type constructors.
Birth_date: DATE ; Address:
Supervisor: EMPLOYEE ; Dept:
DEPARTMENT ;
define type DATE
tuple ( Year:
define type DEPARTMENT
tuple ( Dname:
tuple ( Manager:
EMPLOYEE ; Start_date: DATE ; ) ;
Locations:
set (string) ;
Employees: set (EMPLOYEE) ;
11.1 Overview of Object Database Concepts 361
Parts
» Fundamentals_of_Database_Systems,_6th_Edition
» Characteristics of the Database Approach
» Advantages of Using the DBMS Approach
» A Brief History of Database Applications
» Schemas, Instances, and Database State
» The Three-Schema Architecture
» The Database System Environment
» Centralized and Client/Server Architectures for DBMSs
» Classification of Database Management Systems
» Domains, Attributes, Tuples, and Relations
» Key Constraints and Constraints on NULL Values
» Relational Databases and Relational Database Schemas
» Integrity, Referential Integrity, and Foreign Keys
» Update Operations, Transactions, and Dealing with Constraint Violations
» SQL Data Definition and Data Types
» Specifying Constraints in SQL
» The SELECT-FROM-WHERE Structure of Basic SQL Queries
» Ambiguous Attribute Names, Aliasing, Renaming, and Tuple Variables
» Substring Pattern Matching and Arithmetic Operators
» INSERT, DELETE, and UPDATE Statements in SQL
» Comparisons Involving NULL and Three-Valued Logic
» Nested Queries, Tuples, and Set/Multiset Comparisons
» The EXISTS and UNIQUE Functions in SQL
» Joined Tables in SQL and Outer Joins
» Grouping: The GROUP BY and HAVING Clauses
» Discussion and Summary of SQL Queries
» Specifying General Constraints as Assertions in SQL
» Introduction to Triggers in SQL
» Specification of Views in SQL
» View Implementation, View Update, and Inline Views
» Schema Change Statements in SQL
» Sequences of Operations and the RENAME Operation
» The UNION, INTERSECTION, and MINUS Operations
» The CARTESIAN PRODUCT (CROSS PRODUCT) Operation
» Variations of JOIN: The EQUIJOIN and NATURAL JOIN
» Additional Relational Operations
» Examples of Queries in Relational Algebra
» The Tuple Relational Calculus
» The Domain Relational Calculus
» Using High-Level Conceptual Data Models
» Entity Types, Entity Sets, Keys, and Value Sets
» Relationship Types, Relationship Sets, Roles, and Structural Constraints
» ER Diagrams, Naming Conventions, and Design Issues
» Example of Other Notation: UML Class Diagrams
» Relationship Types of Degree Higher than Two
» Subclasses, Superclasses, and Inheritance
» Constraints on Specialization and Generalization
» Specialization and Generalization Hierarchies
» Modeling of UNION Types Using Categories
» A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions
» Data Abstraction, Knowledge Representation, and Ontology Concepts
» ER-to-Relational Mapping Algorithm
» Discussion and Summary of Mapping for ER Model Constructs
» Mapping EER Model Constructs
» The Role of Information Systems
» The Database Design and Implementation Process
» Use of UML Diagrams as an Aid to Database Design Specification 6
» Rational Rose: A UML-Based Design Tool
» Automated Database Design Tools
» Introduction to Object-Oriented Concepts and Features
» Object Identity, and Objects versus Literals
» Complex Type Structures for Objects and Literals
» Encapsulation of Operations and Persistence of Objects
» Type Hierarchies and Inheritance
» Other Object-Oriented Concepts
» Object-Relational Features: Object Database Extensions to SQL
» Overview of the Object Model of ODMG
» Built-in Interfaces and Classes in the Object Model
» Atomic (User-Defined) Objects
» Extents, Keys, and Factory Objects
» The Object Definition Language ODL
» Differences between Conceptual Design of ODB and RDB
» Mapping an EER Schema to an ODB Schema
» Query Results and Path Expressions
» Overview of the C++ Language Binding in the ODMG Standard
» Structured, Semistructured, and Unstructured Data
» XML Hierarchical (Tree) Data Model
» Well-Formed and Valid XML Documents and XML DTD
» XPath: Specifying Path Expressions in XML
» XQuery: Specifying Queries in XML
» Extracting XML Documents from
» Database Programming: Techniques
» Retrieving Single Tuples with Embedded SQL
» Retrieving Multiple Tuples with Embedded SQL Using Cursors
» Specifying Queries at Runtime Using Dynamic SQL
» SQLJ: Embedding SQL Commands in Java
» Retrieving Multiple Tuples in SQLJ Using Iterators
» Database Programming with SQL/CLI Using C
» JDBC: SQL Function Calls for Java Programming
» Database Stored Procedures and SQL/PSM
» PHP Variables, Data Types, and Programming Constructs
» Overview of PHP Database Programming
» Imparting Clear Semantics to Attributes in Relations
» Redundant Information in Tuples and Update Anomalies
» Normal Forms Based on Primary Keys
» General Definitions of Second and Third Normal Forms
» Multivalued Dependency and Fourth Normal Form
» Join Dependencies and Fifth Normal Form
» Inference Rules for Functional Dependencies
» Minimal Sets of Functional Dependencies
» Properties of Relational Decompositions
» Dependency-Preserving Decomposition
» Dependency-Preserving and Nonadditive (Lossless) Join Decomposition into 3NF Schemas
» Problems with NULL Values and Dangling Tuples
» Discussion of Normalization Algorithms and Alternative Relational Designs
» Further Discussion of Multivalued Dependencies and 4NF
» Other Dependencies and Normal Forms
» Memory Hierarchies and Storage Devices
» Hardware Description of Disk Devices
» Magnetic Tape Storage Devices
» Placing File Records on Disk
» Files of Unordered Records (Heap Files)
» Files of Ordered Records (Sorted Files)
» External Hashing for Disk Files
» Hashing Techniques That Allow Dynamic File Expansion
» Other Primary File Organizations
» Parallelizing Disk Access Using RAID Technology
» Types of Single-Level Ordered Indexes
» Some General Issues Concerning Indexing
» Algorithms for External Sorting
» Implementing the SELECT Operation
» Implementing the JOIN Operation
» Algorithms for PROJECT and Set
» Notation for Query Trees and Query Graphs
» Heuristic Optimization of Query Trees
» Catalog Information Used in Cost Functions
» Examples of Cost Functions for SELECT
» Examples of Cost Functions for JOIN
» Example to Illustrate Cost-Based Query Optimization
» Factors That Influence Physical Database Design
» Physical Database Design Decisions
» An Overview of Database Tuning in Relational Systems
» Transactions, Database Items, Read and Write Operations, and DBMS Buffers
» Why Concurrency Control Is Needed
» Transaction and System Concepts
» Desirable Properties of Transactions
» Serial, Nonserial, and Conflict-Serializable Schedules
» Testing for Conflict Serializability of a Schedule
» How Serializability Is Used for Concurrency Control
» View Equivalence and View Serializability
» Types of Locks and System Lock Tables
» Guaranteeing Serializability by Two-Phase Locking
» Dealing with Deadlock and Starvation
» Concurrency Control Based on Timestamp Ordering
» Multiversion Concurrency Control Techniques
» Validation (Optimistic) Concurrency
» Granularity of Data Items and Multiple Granularity Locking
» Using Locks for Concurrency Control in Indexes
» Other Concurrency Control Issues
» Recovery Outline and Categorization of Recovery Algorithms
» Caching (Buffering) of Disk Blocks
» Write-Ahead Logging, Steal/No-Steal, and Force/No-Force
» Transaction Rollback and Cascading Rollback
» NO-UNDO/REDO Recovery Based on Deferred Update
» Recovery Techniques Based on Immediate Update
» The ARIES Recovery Algorithm
» Recovery in Multidatabase Systems
» Introduction to Database Security Issues 1
» Discretionary Access Control Based on Granting and Revoking Privileges
» Mandatory Access Control and Role-Based Access Control for Multilevel Security
» Introduction to Statistical Database Security
» Introduction to Flow Control
» Encryption and Public Key Infrastructures
» Challenges of Database Security
» Distributed Database Concepts 1
» Types of Distributed Database Systems
» Distributed Database Architectures
» Data Replication and Allocation
» Example of Fragmentation, Allocation, and Replication
» Query Processing and Optimization in Distributed Databases
» Overview of Transaction Management in Distributed Databases
» Overview of Concurrency Control and Recovery in Distributed Databases
» Current Trends in Distributed Databases
» Distributed Databases in Oracle 13
» Generalized Model for Active Databases and Oracle Triggers
» Design and Implementation Issues for Active Databases
» Examples of Statement-Level Active Rules
» Time Representation, Calendars, and Time Dimensions
» Incorporating Time in Relational Databases Using Tuple Versioning
» Incorporating Time in Object-Oriented Databases Using Attribute Versioning
» Temporal Querying Constructs and the TSQL2 Language
» Spatial Database Concepts 24
» Multimedia Database Concepts
» Clausal Form and Horn Clauses
» Datalog Programs and Their Safety
» Evaluation of Nonrecursive Datalog Queries
» Introduction to Information Retrieval
» Types of Queries in IR Systems
» Evaluation Measures of Search Relevance
» Web Analysis and Its Relationship to Information Retrieval
» Analyzing the Link Structure of Web Pages
» Approaches to Web Content Analysis
» Trends in Information Retrieval
» Data Mining as a Part of the Knowledge
» Goals of Data Mining and Knowledge Discovery
» Types of Knowledge Discovered during Data Mining
» Market-Basket Model, Support, and Confidence
» Frequent-Pattern (FP) Tree and FP-Growth Algorithm
» Other Types of Association Rules
» Approaches to Other Data Mining Problems
» Commercial Data Mining Tools
» Data Modeling for Data Warehouses
» Difficulties of Implementing Data Warehouses
» Grouping, Aggregation, and Database Modification in QBE
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