Generalization in dbms

The exact difference is as follows. You have to first separate the total/partial participation constraints to understand this better and we'll take them into account later on. Disjoint Constraint Any instance can map to at most one subclass. Not more than that. e.g Bank Account can be either 'Savings Account' or 'Current Account' not both. So when the database is operational, every given instance will be mapped to exactly one subclass defined under the super class. Another example would a meal will be mapped to either Veg or Non-veg..It can't be both. Partial Constraint Any instance may or may not map to multiple sub classes of a given super class. This usually happens when an instance play multiple roles and not limited to a single one. e.g Employee may map to either supervisor, manager or both. This means an employee can play both the roles of a manager and a supervisor. Another example would be a musician who maybe mapping to either violin player, guitar player, flutist,saxophonist or all of them. Note: So when you specify an 'ISA' relationship your subclasses may behave in either disjoint way or overlap way.. They can't be both, meaning that Disjoint is the exact opposite of Overlap constraint. Now let's focus on Total and Partial constraints. Regardless of the overlapping/disjoint constraints, total/partial mean 'do all the instances support the specialization?' question. So when the database is operational and if your ISA relationship is total, any instance coming wi...

Important Points • Generalization is the bottom-up approach in which two or more lower-level entities are combined based on some common properties to make a higher-level entity. • In Generalization, an entity of a higher level can also combine with the entities of a lower level to form a different higher-level entity. • The process of Generalization is more like subclass and Superclass, but the only difference is the approach that uses the bottom-up approach. • In Generalization, entities are combined to form a more generalized entity, i.e., subclasses are combined to make a superclass. For example, Employee, Teacher, and Doctor entities can be generalized and create a higher level entity Person with some common properties like Name, Age, Phone, and Address. Specialization Specialization is the top-down process where a higher-level entity is broken down into two lower-level entities. On the other hand, specialization splits an entity into multiple levels that inherit some properties from the root node, i.e., the head entity. Specialization is the opposite of Generalization, which aims to increase the size of schema/entities. Important Points • Specialization is the opposite of Generalization. It follows the top-to-down approach in which one higher entity level is split into lower levels of entities. • Specialization is used to find the subsets of entities that share different properties. • Here, the superclass is defined first, then the subclass and its related attributes ...

Prerequisite – Today the complexity of the data is increasing so it becomes more and more difficult to use the traditional ER model for database modeling. To reduce this complexity of modeling we have to make improvements or enhancements to the existing ER model to make it able to handle the complex application in a better way. Enhanced entity-relationship diagrams are advanced database diagrams very similar to regular ER diagrams which represent the requirements and complexities of complex databases. It is a diagrammatic technique for displaying the Sub Class and Super Class; Specialization and Generalization; Union or Category; Aggregation etc. Generalization and Specialization: These are very common relationships found in real entities. However, this kind of relationship was added later as an enhanced extension to the classical ER model. Specialized classes are often called subclass while a generalized class is called a superclass, probably inspired by object-oriented programming. A sub-class is best understood by “IS-A analysis”. The following statements hopefully make some sense to your mind “Technician IS-A Employee”, and “Laptop IS-A Computer”. An entity is a specialized type/class of another entity. For example, a Technician is a special Employee in a university system Faculty is a special class of Employees. We call this phenomenon generalization/specialization. In the example here Employee is a generalized entity class while the Technician and Faculty are special...

Since the 1980s, there has been a rapid amplification in the development of many new database systems that have more demanding database requirements than those of the traditional applications. As the basic concepts of ER modeling are often not enough to represent the requirements of the newer complex applications, which therefore stimulated the need to develop additional 'semantic' modeling concepts. Various semantic data models have been proposed, and some of the most important semantic concepts have been successfully incorporated into the original ER model. The ER model, supported with additional semantic concepts, is called the Enhanced Entity-Relationship (EER) model. There are three of the most important and useful added concepts of the EER model, namely specialization/generalization, aggregation, and composition. In this chapter, you will learn about the main two important concepts. These are: • Generalization and • Aggregation What is Generalization / Specialization? The concept of generalization (specialization) is associated with special types of entities known as superclasses and subclasses, and the process of attribute inheritance. Database managers begin this section by defining what superclasses and subclasses are and by examining superclass/subclass relationships. The ER Model has the capability of articulating database entities in a conceptual hierarchical manner. As the hierarchy goes up, it generalizes the view of entities, and as you go deep in the hierar...

1. Data cube approach : • It is also known as OLAP approach. • It is an efficient approach as it is helpful to make the past selling graph. • In this approach, computation and results are stored in the Data cube. • It uses Roll-up and Drill-down operations on a data cube. • These operations typically involve aggregate functions, such as count(), sum(), average(), and max(). • These materialized views can then be used for decision support, knowledge discovery, and many other applications. 2. Attribute oriented induction : • It is an online data analysis, query oriented and generalization based approach. • In this approach, we perform generalization on basis of different values of each attributes within the relevant data set. after that same tuple are merged and their respective counts are accumulated in order to perform aggregation. • It performs off-line aggregation before an OLAP or data mining query is submitted for processing. • On the other hand, the attribute oriented induction approach, at least in its initial proposal, a relational database query – oriented, generalized based (on-line data analysis technique). • It is not limited to particular measures nor categorical data. • Attribute oriented induction approach uses two method : (i). Attribute removal. (ii). Attribute generalization.