Week 3 Classification of Database Management Systems & Data Modeling
- 1. Classification of Database Management Systems
- 2. 2 The most popular data model in use today is the relational data model. Well-known DBMSs like Oracle, MS SQL Server, DB2 and MySQL support this model. Other traditional models, such as hierarchical data models network data models, etc… They are not commonly used due to their complexity. These are all referred to as traditional models because they preceded the relational model. Classification of Database Management Systems 2
- 3. 3 1. Increased effectiveness – The data model increases the effectiveness of the database. Because the data it contains is very real, reliable and extensible. 2. Reduced costs – We can build database applications through data models at a very low cost. For the data model, we have to spend only 10 percent of the project budget. With which we save 70 percent of the budget. 3. simplicity – data models are designed so that there is no problem in accessing the database. Its interface is very simple so that most of them are able to use it easily. Classification of Database Management Systems 3 Advantage of a data model
- 4. 4 4. Minimum redundancy – Redundancy means “duplication of data” ie the same type of data being present in two places. It is very harmful to have duplicate data that is stored unnecessarily in the database. The data models that are used greatly reduce redundancy. 5. data integrity – Data models do not allow any user to access the database without the owner’s permission. Any user can access the database only when he proves his integrity. 6. In data independence – Data-models, any data is independent of the database. Even if a change Classification of Database Management Systems 4 Advantage of a data model
- 5. 5 7. faster performance – If the data model is good then the performance of the database becomes very fast. Because the database is built according to the data models. 8. Reduced errors – DBMS has two types of errors – 1) database application error 2) data error. The data model reduces both these errors very much and it improves the quality of the data. 9. Reduced risk – Through the data-model, we can reduce the risks of the database. The data- model estimates the complexity of the database and analyzes the entire list Classification of Database Management Systems 5 Advantage of a data model
- 6. Types of Data Model Advantage vs Disadvantages
- 7. 7 Hierarchical Database model In this model, there is a parent-child relationship. In this model, each entity has only one parent and many abstract children. There is only one entity in this model that we call root. Classification of Database Management Systems 7
- 8. 8 Hierarchical Database model Advantage of the hierarchical model It promotes data sharing. There is a parent/child relationship due to which its concepts are simple. It provides database security. It takes 1 to many relationships. A disadvantage of the hierarchical model It is not flexible It does not have a data definition and data manipulation languages. Classification of Database Management Systems 8
- 9. 9 NETWORK DATABASE MODEL In a network model, data is organized into graphs. And it can have more than one parent node. That is, there is more parent/child relationship in it. And in it, some entity can access from multiple paths. So we can say that in this model we store and access the data as a network. Classification of Database Management Systems 9
- 10. 10 Network Database model Advantage of a network model Its concept is as simple as the hierarchical model. There is more than one parent/child relationship. Data can be accessed easily in it. It provides data integrity. It contains a data definition language (DDL) and data manipulation language (DML). A disadvantage of the network model Its database structure is very complex (difficult) because all the records in it are maintained using pointers. Changes in its structure require changes in all programs. Classification of Database Management Systems 10
- 11. 11 RELATIONAL Database MODEL In this model, data is stored in relations i.e. tables and each relation has rows and columns. A relational model is a group of tables in which data and relationships are specified. In this, the data is stored in two-dimensional tables, the tables are also called a relation. And the row of each table is called a tuple. The tuple represents the entity and the column of the table represents the attribute. Classification of Database Management Systems 11
- 12. 12 Relational Database model Advantage of the relational model It is very flexible, it can easily make any kind of change. In this, the data is kept in tables, so its concept is very simple. It provides data integrity. That is, no user can access the database without the owner’s permission. A disadvantage of the relational model It requires powerful hardware computers, storage devices, and software. It is very easy to use but when a user stores data in it incorrectly then it becomes very bad DBMS. This is a very simple model, due to its simplicity, some users create their own database, causing the problem of data inconsistency, data duplication. Classification of Database Management Systems 12
- 13. 13 E-R MODEL The full name of the E-R model is the entity-relationship model. It is a graphical approach to database design. That is, the database is designed by diagram and visual representation in it. It uses entity/relationship to display real-world objects. Entity – In the ER model, an entity is a real-world object. The properties of entities are called attributes. For example, a student is an entity in the database of SCHOOL. Relationship – The association between entities is called a relationship. The mapping cordialities define the very essence association- one to one one to many Classification of Database Management Systems 13
- 14. 14 Advantage of the E-R model The E-R model is very simple if we know the relationship between entities and attributes. This model is presented as a diagram. With which we can understand easily. There is no data manipulation. Its design is of a high level. Classification of Database Management Systems 14
- 15. Data Modelling
- 16. Data Modeling Data modeling: is a technique for organizing and documenting a system’s DATA. Data modeling is sometimes called database modeling because a data model is usually implemented as a database.
- 17. Entity Relational Diagram (ERD) An ERD depicts data in terms of the entities and relationships described by the data.
- 18. Entities An entity is something about which we want to store data. An entity is a class of persons, places, objects, events, or concepts about which we need to capture and store data. An entity instance is a single occurrence of an entity.
- 19. Attributes - Identification An attribute is a descriptive property or characteristic of an entity. Synonyms include element, property, and field Every entity must have an identifier or key. An key is an attribute, or a group of attributes, which assumes a unique value for each entity instance. It is sometimes called an identifier. Sometimes more than one attribute is required to uniquely identify an instance of an entity. A group of attributes that uniquely identifies an instance of an entity is called a concatenated key. Synonyms include composite key and compound key.
- 20. Relationships A relationship is a natural business association that exists between one or more entities. The relationship may represent an event that links the entities, or merely a logical affinity that exists between the entities. A connecting line between two entities on an ERD represents a relationship. A verb phrase describes the relationship. All relationships are implicitly bidirectional, meaning that they can interpreted in both directions.
- 21. 06.5.18
- 22. 06.5.18 Cardinality Each relationship on an ERD also depicts the complexity or degree of each relationship, and this is called cardinality. Cardinality defines the minimum and maximum number of occurrences of one entity for a single occurrence of the related entity. Because all relationships are bi-directional, cardinality must be defined in both directions for every relationship.
- 23. Cardinality Interpretation Minimum Instances Maximum Instances Graphic Notation Exactly one 1 1 Zero or one 0 1 One or more 1 many ( > 1 ) Zero, one, or more 0 many ( > 1 ) More than one > 1 > 1 Figure 5.3
- 24. Foreign Keys A relationship implies that instances of one entity are related to instances of another entity. To be able to identify those instances for any given entity, the primary key of one entity must be migrated into the other entity as a foreign key. A foreign key is a primary key of one entity that is contributed to (duplicated in) another entity for the purpose of identifying instances of a relationship. A foreign key (always in a child entity) always matches the primary key (in a parent entity).
- 25. 06.5.18 One customer must be associated with cero or multiples orders
- 26. 06.5.18 (A: SOLVE CARDINALITY:) (B: SOLVE CARDINALITY:)
- 27. How to Construct Data Models 1st Step - Entity Discovery The first task is to discover those fundamental entities in the system. A true entity has multiple instances—dozens, hundreds, thousands, or more! Entities should be named with nouns that describe the person, event, place, or tangible thing about which we want to store data. Try not to abbreviate or use acronyms. Names should be singular so as to distinguish the logical concept of the entity from the actual instances of the entity. Define each entity in business terms. Don’t define the entity in technical terms, and don’t define it as ‘data about …’. Your entity names and definitions should establish an initial glossary of business terminology that will serve both you and future analysts and users for years to come.
- 28. 2nd Step - The Context Data Model The second task in data modeling is to construct the context data model. The context data model includes the fundamental or independent entities that were previously discovered. • An independent entity is one which exists regardless of the existence of any other entity. Its primary key contain no attributes that would make it dependent on the existence of another entity. Relationships should be named with verb phrases that, when combined with the entity names, form simple business sentences or assertions. • If only one-way naming is used, always name the relationship from parent-to-child. How to Construct Data Models
- 29. 06.5.18 How to Construct Data Models
- 30. 3rd Step - The Key-Based Data Model The third task is to identify the keys of each entity. If you cannot define keys for an entity, it may be that the entity doesn’t really exist— that is, multiple occurrences of the so-called entity do not exist. How to Construct Data Models
- 31. 4th Step - Generalized Hierarchies At this time, it would be useful to identify any generalization hierarchies in a business problem. the process of generalizing entities, where the generalized entities contain the properties of all the generalized entities, is called generalization. How to Construct Data Models
- 32. 5th Step - The Fully Attributed Data Model The fifth task is to identify the remaining data attributes. Each attribute should be mapped to only one entity. Foreign keys are the exception – they identify associated instances of related entities. How to Construct Data Models
- 33. 06.5.18 6th Step - The Fully Described Model The last task is to fully describe the data model. Most tools provide extensive facilities for describing the data types, domains, and defaults for all attributes to the repository. Additional descriptive properties may be recorded for attributes such as: Who should be able to create, delete, update, and access each attribute? How long should each attribute (or entity) be kept before the data is deleted or archived? How to Construct Data Models
- 34. Copyright