1. Object-based Logical Models.
2. Record-based Logical Models.
3. Physical Data Models.

We’ll look at them in more detail now.

• Describe data at the conceptual and view levels.
• Provide fairly flexible structuring capabilities.
• Allow one to specify data constraints explicitly.
• Over 30 such models, including
  • Entity-relationship model.
  • Object-oriented model.
  • Binary model.
  • Semantic data model.
  • Infological model.
  • Functional data model.

• An entity is a distinguishable object that exists.
• Each entity has associated with it a set of attributes describing it.
• E.g. number and balance for an account entity.
• A relationship is an association among several entities.
• e.g. A cust_acct relationship associates a customer with each account he or she has.
• The set of all entities or relationships of the same type is called the entity set or relationship set.
• Another essential element of the E-R diagram is the mapping cardinalities, which express the number of entities to which another entity can be associated via a relationship set.

We’ll see later how well this model works to describe real world situations.

• rectangles: represent entity sets.
• ellipses: represent attributes.
• diamonds: represent relationships among entity sets.
• lines: link attributes to entity sets and entity sets to relationships.

• An object contains values stored in instance variables within the object.
• Unlike the record-oriented models, these values are themselves objects.
• Thus objects contain objects to an arbitrarily deep level of nesting.
• An object also contains bodies of code that operate on the the object.
• These bodies of code are called methods.
• Objects that contain the same types of values and the same methods are grouped into classes.
• A class may be viewed as a type definition for objects.
• Analogy: the programming language concept of an abstract data type.
• The only way in which one object can access the data of another object is by invoking the method of that other object.
• This is called sending a message to the object.
• Internal parts of the object, the instance variables and method code, are not visible externally.
• Result is two levels of data abstraction.

For example, consider an object representing a bank account.

• The object contains instance variables number and balance.
• The object contains a method pay-interest which adds interest to the balance.
• Under most data models, changing the interest rate entails changing code in application programs.
• In the object-oriented model, this only entails a change within the pay-interest method.

• Two objects containing the same values are distinct.
• Distinction is created and maintained in physical level by assigning distinct object identifiers.

• Also describe data at the conceptual and view levels.
• Unlike object-oriented models, are used to
  • Specify overall logical structure of the database, and
  • Provide a higher-level description of the implementation.
• Named so because the database is structured in fixed-format records of several types.
• Each record type defines a fixed number of fields, or attributes.
• Each field is usually of a fixed length (this simplifies the implementation).
• Record-based models do not include a mechanism for direct representation of code in the database.
• Separate languages associated with the model are used to express database queries and updates.
• The three most widely-accepted models are the relational, network, and hierarchical.
• This course will concentrate on the relational model.
• The network and hierarchical models are covered in appendices in the text.

• Unifying model.
• Frame memory.