Atomic (User-Defined) Objects
11.3.4 Atomic (User-Defined) Objects
The previous section described the built-in collection types of the object model. Now we discuss how object types for atomic objects can be constructed. These are specified using the keyword class in ODL. In the object model, any user-defined object that is not a collection object is called an atomic object. 31
For example, in a UNIVERSITY database application, the user can specify an object type (class) for STUDENT objects. Most such objects will be structured objects; for example, a STUDENT object will have a complex structure, with many attributes, relationships, and operations, but it is still considered atomic because it is not a col- lection. Such a user-defined atomic object type is defined as a class by specifying its properties and operations. The properties define the state of the object and are fur- ther distinguished into attributes and relationships. In this subsection, we elabo- rate on the three types of components—attributes, relationships, and operations—that a user-defined object type for atomic (structured) objects can include. We illustrate our discussion with the two classes EMPLOYEE and DEPARTMENT shown in Figure 11.7.
An attribute is a property that describes some aspect of an object. Attributes have values (which are typically literals having a simple or complex structure) that are stored within the object. However, attribute values can also be Object_id s of other objects. Attribute values can even be specified via methods that are used to calculate
the attribute value. In Figure 11.7 32 the attributes for EMPLOYEE are Name , Ssn , Birth_date , Sex , and Age , and those for DEPARTMENT are Dname , Dnumber , Mgr , Locations , and Projs . The Mgr and Projs attributes of DEPARTMENT have complex structure and are defined via struct , which corresponds to the tuple constructor of Section 11.1.3. Hence, the value of Mgr in each DEPARTMENT object will have two components: Manager , whose value is an Object_id that references the EMPLOYEE object that manages the DEPARTMENT , and Start_date , whose value is a date . The locations attribute of DEPARTMENT is defined via the set constructor, since each DEPARTMENT object can have a set of locations.
A relationship is a property that specifies that two objects in the database are related. In the object model of ODMG, only binary relationships (see Section 7.4) are explicitly represented, and each binary relationship is represented by a pair of inverse references specified via the keyword relationship. In Figure 11.7, one relationship exists that relates each EMPLOYEE to the DEPARTMENT in which he or she works— the Works_for relationship of EMPLOYEE . In the inverse direction, each DEPARTMENT is related to the set of EMPLOYEES that work in the DEPARTMENT — the Has_emps relationship of DEPARTMENT . The keyword inverse specifies that these two properties define a single conceptual relationship in inverse directions. 33
31 As mentioned earlier, this definition of atomic object in the ODMG object model is different from the definition of atom constructor given in Section 11.1.3, which is the definition used in much of the object-
oriented database literature. 32 We are using the Object Definition Language (ODL) notation in Figure 11.7, which will be discussed in
11.3 The ODMG Object Model and the Object Definition Language ODL 387
class EMPLOYEE
Figure 11.7
( extent
The attributes, relationships, key
ALL_EMPLOYEES
and operations in a class {
Ssn )
definition. attribute
Birth_date;
attribute
enum Gender{M , F} Sex;
Works_for
inverse DEPARTMENT::Has_emps;
void
reassign_emp(in string New_dname)
raises(dname_not_valid);
}; class DEPARTMENT (
extent
ALL_DEPARTMENTS
key
Dname , Dnumber )
struct Dept_mgr {EMPLOYEE Manager , date Start_date}
Mgr;
attribute
set<string>
Locations;
attribute
struct Projs {string Proj_name , time Weekly_hours)
Projs;
relationship
Has_emps inverse EMPLOYEE::Works_for; void
set<EMPLOYEE>
add_emp(in string New_ename) raises(ename_not_valid);
void
change_manager(in string New_mgr_name; in date Start_date);
By specifying inverses, the database system can maintain the referential integrity of the relationship automatically. That is, if the value of Works_for for a particular EMPLOYEE
D, then the value of Has_emps for DEPARTMENT
E refers to DEPARTMENT
D must include a reference to E in its set of EMPLOYEE references. If
the database designer desires to have a relationship to be represented in only one direction, then it has to be modeled as an attribute (or operation). An example is the Manager component of the Mgr attribute in DEPARTMENT .
In addition to attributes and relationships, the designer can include operations in object type (class) specifications. Each object type can have a number of operation signatures , which specify the operation name, its argument types, and its returned value, if applicable. Operation names are unique within each object type, but they
388 Chapter 11 Object and Object-Relational Databases
occur during operation execution. The implementation of the operation will include the code to raise these exceptions. In Figure 11.7 the EMPLOYEE class has one operation: reassign_emp , and the DEPARTMENT class has two operations: add_emp and change_manager .
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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