The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
The document provides an overview of entity-relationship (E-R) modeling concepts including:
- Entity sets represent collections of real-world entities that share common properties
- Relationship sets define associations between entity sets
- Attributes provide additional information about entities and relationships
- Keys uniquely identify entities and relationships
- Cardinalities constrain how entities can participate in relationships
- E-R diagrams visually depict entity sets, attributes, relationships and constraints.
The document provides an overview of entity-relationship (ER) modeling concepts used in database design. It defines key terms like entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also discusses entity types, relationship degrees, key attributes, weak entities, and how to model one-to-one, one-to-many, many-to-one, and many-to-many relationships. Overall, the document serves as a guide to basic ER modeling principles for conceptual database design.
Entity Relationship Diagrams (ERDs) are conceptual data models used in software engineering to model information systems. ERDs represent entities as rectangles, attributes as ellipses, and relationships as diamonds connecting entities. Attributes can be single-valued, multi-valued, composite, or derived. Relationships have cardinality like one-to-one, one-to-many, many-to-one, or many-to-many. Participation constraints and Codd's 12 rules of relational databases are also discussed in the document.
The document presents information on Entity Relationship (ER) modeling for database design. It discusses the key concepts of ER modeling including entities, attributes, relationships and cardinalities. It also explains how to create an Entity Relationship Diagram (ERD) using standard symbols and notations. Additional features like generalization, specialization and inheritance are covered which allow ERDs to represent hierarchical relationships between entities. The presentation aims to provide an overview of ER modeling and ERDs as an important technique for conceptual database design.
The document provides an overview of database systems, including their purpose, components, and architecture. It describes how database systems offer solutions to problems with using file systems to store data by providing data independence, concurrency control, recovery from failures, and more. It also defines key concepts like data models, data definition and manipulation languages, transactions, storage management, database users, administrators, and the roles they play in overall database system structure.
The document provides information on the key concepts of an entity-relationship (E-R) model, including:
1) Entities represent real-world objects like people, places, and things that are stored in a database. Attributes describe the properties of entities.
2) Relationships represent associations between entities. Relationships have properties like degree, cardinality, and existence.
3) Keys like primary keys and foreign keys uniquely identify entities and define relationships between entities.
4) Strong and weak entities differ in whether they have their own primary keys or rely on other entities.
5) E-R diagrams visually depict entities, attributes, relationships, keys and other concepts to model a database.
Integrity constraints are rules that help maintain data quality and consistency in a database. The main types of integrity constraints are:
1. Domain constraints specify valid values and data types for attributes to restrict what data can be entered.
2. Entity constraints require that each row have a unique identifier and prevent null values in primary keys.
3. Referential integrity constraints maintain relationships between tables by preventing actions that would invalidate links between foreign and primary keys.
4. Cascade rules extend referential integrity by automatically propagating updates or deletes from a primary table to its related tables.
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
The document provides an overview of entity-relationship (E-R) modeling concepts including:
- Entity sets represent collections of real-world entities that share common properties
- Relationship sets define associations between entity sets
- Attributes provide additional information about entities and relationships
- Keys uniquely identify entities and relationships
- Cardinalities constrain how entities can participate in relationships
- E-R diagrams visually depict entity sets, attributes, relationships and constraints.
The document provides an overview of entity-relationship (ER) modeling concepts used in database design. It defines key terms like entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also discusses entity types, relationship degrees, key attributes, weak entities, and how to model one-to-one, one-to-many, many-to-one, and many-to-many relationships. Overall, the document serves as a guide to basic ER modeling principles for conceptual database design.
Entity Relationship Diagrams (ERDs) are conceptual data models used in software engineering to model information systems. ERDs represent entities as rectangles, attributes as ellipses, and relationships as diamonds connecting entities. Attributes can be single-valued, multi-valued, composite, or derived. Relationships have cardinality like one-to-one, one-to-many, many-to-one, or many-to-many. Participation constraints and Codd's 12 rules of relational databases are also discussed in the document.
The document presents information on Entity Relationship (ER) modeling for database design. It discusses the key concepts of ER modeling including entities, attributes, relationships and cardinalities. It also explains how to create an Entity Relationship Diagram (ERD) using standard symbols and notations. Additional features like generalization, specialization and inheritance are covered which allow ERDs to represent hierarchical relationships between entities. The presentation aims to provide an overview of ER modeling and ERDs as an important technique for conceptual database design.
The document provides an overview of database systems, including their purpose, components, and architecture. It describes how database systems offer solutions to problems with using file systems to store data by providing data independence, concurrency control, recovery from failures, and more. It also defines key concepts like data models, data definition and manipulation languages, transactions, storage management, database users, administrators, and the roles they play in overall database system structure.
The document provides information on the key concepts of an entity-relationship (E-R) model, including:
1) Entities represent real-world objects like people, places, and things that are stored in a database. Attributes describe the properties of entities.
2) Relationships represent associations between entities. Relationships have properties like degree, cardinality, and existence.
3) Keys like primary keys and foreign keys uniquely identify entities and define relationships between entities.
4) Strong and weak entities differ in whether they have their own primary keys or rely on other entities.
5) E-R diagrams visually depict entities, attributes, relationships, keys and other concepts to model a database.
Integrity constraints are rules that help maintain data quality and consistency in a database. The main types of integrity constraints are:
1. Domain constraints specify valid values and data types for attributes to restrict what data can be entered.
2. Entity constraints require that each row have a unique identifier and prevent null values in primary keys.
3. Referential integrity constraints maintain relationships between tables by preventing actions that would invalidate links between foreign and primary keys.
4. Cascade rules extend referential integrity by automatically propagating updates or deletes from a primary table to its related tables.
This document discusses different types of keys used in databases. It defines keys as attributes that uniquely identify rows in tables. It then explains various key types including primary keys, candidate keys, super keys, alternate keys, unique keys, composite keys, foreign keys, natural keys and surrogate keys. For each key type, it provides examples from sample tables and discusses their properties and how they differ from each other. The document concludes that databases generally only contain primary, foreign, unique and surrogate keys, while other key types are conceptual, and that each table requires a unique key to reliably access and identify data.
Dbms Notes Lecture 9 : Specialization, Generalization and AggregationBIT Durg
This document discusses key concepts in the Extended Entity Relationship (EER) model, including specialization, generalization, attribute inheritance, and aggregation. Specialization involves dividing a higher-level entity set into lower-level subsets, while generalization groups multiple lower-level entity sets into a single higher-level set based on common attributes. Attribute inheritance allows attributes to be passed from higher to lower levels. Aggregation models relationships between relationships by treating them as higher-level entities. The document provides examples and discusses constraints like disjointness and completeness that can be applied.
Functional dependency defines a relationship between attributes in a table where a set of attributes determine another attribute. There are different types of functional dependencies including trivial, non-trivial, multivalued, and transitive. An example given is a student table with attributes Stu_Id, Stu_Name, Stu_Age which has the functional dependency of Stu_Id->Stu_Name since the student ID uniquely identifies the student name.
The document discusses the entity relationship (ER) model used for conceptual database design. It describes the key components of an ER diagram including entities represented as rectangles, attributes described as ovals, and relationships shown as diamonds. Different types of relationships are also defined such as one-to-one, one-to-many, many-to-one, and many-to-many. The ER model provides a way to design and visualize the entities, attributes, and relationships within a database in a simple diagram.
An Entity–relationship model (ER model) describes the structure of a database with the help of a diagram, which is known as Entity Relationship Diagram (ER Diagram). An ER model is a design or blueprint of a database that can later be implemented as a database. The main components of E-R model are: entity set and relationship set
This document provides an overview of entity-relationship modeling as a first step for designing a relational database. It describes how to model entities, attributes, relationships, and participation constraints. Key aspects covered include using boxes to represent entity types, diamonds for relationship types, and labeling relationships with degrees. The document also discusses handling multi-valued attributes and deciding whether to model concepts as attributes or entity types.
This document discusses the different types of keys used in database management systems (DBMS). It outlines five types of keys: super key, candidate key, primary key, foreign key, and composite key. For each key type, it provides a definition and examples to illustrate how the key uniquely identifies records in a database table. The primary points made are that super keys are the broadest unique identifiers, candidate keys are minimal super keys, primary keys must uniquely identify each record and not change, and foreign keys link records between related tables.
This document discusses the entity-relationship (ER) model for conceptual database design. It defines key concepts like entities, attributes, relationships, keys, and participation constraints. Entities can be strong or weak, and attributes can be simple, composite, multi-valued, or derived. Relationships associate entities and can specify cardinality like one-to-one, one-to-many, or many-to-many. The ER model diagrams the structure and constraints of a database before its logical and physical implementation.
Dbms architecture
Three level architecture is also called ANSI/SPARC architecture or three schema architecture
This framework is used for describing the structure of specific database systems (small systems may not support all aspects of the architecture)
In this architecture the database schemas can be defined at three levels explained in next slide
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
The document discusses the relational data model and query languages. It provides the following key points:
1. The relational data model organizes data into tables with rows and columns, where rows represent records and columns represent attributes. Relations between data are represented through tables.
2. Relational integrity constraints include key constraints, domain constraints, and referential integrity constraints to ensure valid data.
3. Relational algebra and calculus provide theoretical foundations for query languages like SQL. Relational algebra uses operators like select, project, join on relations, while relational calculus specifies queries using logic.
This document discusses database normalization and different normal forms including 1NF, 2NF, 3NF, and BCNF. It defines anomalies like insertion, update, and deletion anomalies that can occur when data is not normalized. Examples are provided to illustrate the different normal forms and how denormalizing data can lead to anomalies. The key aspects of each normal form like removing repeating groups (1NF), removing functional dependencies on non-prime attributes (2NF), and removing transitive dependencies (3NF, BCNF) are explained.
The document discusses data modeling, which involves creating a conceptual model of the data required for an information system. There are three types of data models - conceptual, logical, and physical. A conceptual data model describes what the system contains, a logical model describes how the system will be implemented regardless of the database, and a physical model describes the implementation using a specific database. Common elements of a data model include entities, attributes, and relationships. Data modeling is used to standardize and communicate an organization's data requirements and establish business rules.
The document outlines a 7-step process for mapping an entity-relationship (ER) schema to a relational database schema. The steps include mapping regular and weak entity types, binary 1:1, 1:N, and M:N relationship types, multivalued attributes, and n-ary relationship types to tables. For each type of schema element, the document describes how to represent it as a table with primary keys and foreign key attributes that preserve the relationships in the original ER schema.
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
The document discusses the drawbacks of using file systems to manage large amounts of shared data, such as data redundancy, inconsistency, isolation, and lack of security and crash recovery. It then introduces database management systems (DBMS) as an alternative that offers advantages like data independence, efficient access, integrity, security, concurrent access, administration, and reduced application development time. However, DBMS also have disadvantages including cost, size, complexity, and higher impact of failure.
The document outlines the steps for mapping an ER or EER model to a relational database schema. It discusses:
1. The 7 steps for mapping entity types, relationship types, attributes, and other constructs from an ER model to relations. This includes mapping entities, relationships, attributes, specializations/generalizations.
2. Additional steps 8 and 9 for mapping special constructs from an EER model like specialization/generalization and categories/union types. Various options for mapping these constructs are presented.
3. Examples are provided throughout to illustrate how each modeling construct in sample ER/EER diagrams would be mapped to relations and keys following the outlined steps. Figures show both the ER/EER
This document describes the three level architecture of a database management system (DBMS): the external, conceptual, and internal levels. The external level defines different views of the database for users. The conceptual level defines the logical structure and relationships of the entire database. The internal level defines the physical storage and implementation of the data. The document also discusses logical and physical data independence, which refer to the ability to modify schemas at different levels without affecting higher levels.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
The document discusses entity relationship diagrams and database design. It defines key concepts such as entities, attributes, relationships and cardinalities. Entities can have single-valued or multi-valued attributes. Relationships connect entities and can be one-to-one, one-to-many, many-to-one, or many-to-many. Primary keys uniquely identify entities and foreign keys define relationships between entities. Together these elements form a conceptual model of entities and their relationships within a database.
The document discusses Entity Relationship Diagrams (ERDs) which are used for data modeling. It defines ERD components such as data objects, attributes, and relationships. It describes different types of relationships between entities like one-to-one, one-to-many, and many-to-many relationships. Cardinality and degree of relationships are also explained. Examples of ERD diagrams and benefits like effective communication are provided.
This document discusses different types of keys used in databases. It defines keys as attributes that uniquely identify rows in tables. It then explains various key types including primary keys, candidate keys, super keys, alternate keys, unique keys, composite keys, foreign keys, natural keys and surrogate keys. For each key type, it provides examples from sample tables and discusses their properties and how they differ from each other. The document concludes that databases generally only contain primary, foreign, unique and surrogate keys, while other key types are conceptual, and that each table requires a unique key to reliably access and identify data.
Dbms Notes Lecture 9 : Specialization, Generalization and AggregationBIT Durg
This document discusses key concepts in the Extended Entity Relationship (EER) model, including specialization, generalization, attribute inheritance, and aggregation. Specialization involves dividing a higher-level entity set into lower-level subsets, while generalization groups multiple lower-level entity sets into a single higher-level set based on common attributes. Attribute inheritance allows attributes to be passed from higher to lower levels. Aggregation models relationships between relationships by treating them as higher-level entities. The document provides examples and discusses constraints like disjointness and completeness that can be applied.
Functional dependency defines a relationship between attributes in a table where a set of attributes determine another attribute. There are different types of functional dependencies including trivial, non-trivial, multivalued, and transitive. An example given is a student table with attributes Stu_Id, Stu_Name, Stu_Age which has the functional dependency of Stu_Id->Stu_Name since the student ID uniquely identifies the student name.
The document discusses the entity relationship (ER) model used for conceptual database design. It describes the key components of an ER diagram including entities represented as rectangles, attributes described as ovals, and relationships shown as diamonds. Different types of relationships are also defined such as one-to-one, one-to-many, many-to-one, and many-to-many. The ER model provides a way to design and visualize the entities, attributes, and relationships within a database in a simple diagram.
An Entity–relationship model (ER model) describes the structure of a database with the help of a diagram, which is known as Entity Relationship Diagram (ER Diagram). An ER model is a design or blueprint of a database that can later be implemented as a database. The main components of E-R model are: entity set and relationship set
This document provides an overview of entity-relationship modeling as a first step for designing a relational database. It describes how to model entities, attributes, relationships, and participation constraints. Key aspects covered include using boxes to represent entity types, diamonds for relationship types, and labeling relationships with degrees. The document also discusses handling multi-valued attributes and deciding whether to model concepts as attributes or entity types.
This document discusses the different types of keys used in database management systems (DBMS). It outlines five types of keys: super key, candidate key, primary key, foreign key, and composite key. For each key type, it provides a definition and examples to illustrate how the key uniquely identifies records in a database table. The primary points made are that super keys are the broadest unique identifiers, candidate keys are minimal super keys, primary keys must uniquely identify each record and not change, and foreign keys link records between related tables.
This document discusses the entity-relationship (ER) model for conceptual database design. It defines key concepts like entities, attributes, relationships, keys, and participation constraints. Entities can be strong or weak, and attributes can be simple, composite, multi-valued, or derived. Relationships associate entities and can specify cardinality like one-to-one, one-to-many, or many-to-many. The ER model diagrams the structure and constraints of a database before its logical and physical implementation.
Dbms architecture
Three level architecture is also called ANSI/SPARC architecture or three schema architecture
This framework is used for describing the structure of specific database systems (small systems may not support all aspects of the architecture)
In this architecture the database schemas can be defined at three levels explained in next slide
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
The document discusses the relational data model and query languages. It provides the following key points:
1. The relational data model organizes data into tables with rows and columns, where rows represent records and columns represent attributes. Relations between data are represented through tables.
2. Relational integrity constraints include key constraints, domain constraints, and referential integrity constraints to ensure valid data.
3. Relational algebra and calculus provide theoretical foundations for query languages like SQL. Relational algebra uses operators like select, project, join on relations, while relational calculus specifies queries using logic.
This document discusses database normalization and different normal forms including 1NF, 2NF, 3NF, and BCNF. It defines anomalies like insertion, update, and deletion anomalies that can occur when data is not normalized. Examples are provided to illustrate the different normal forms and how denormalizing data can lead to anomalies. The key aspects of each normal form like removing repeating groups (1NF), removing functional dependencies on non-prime attributes (2NF), and removing transitive dependencies (3NF, BCNF) are explained.
The document discusses data modeling, which involves creating a conceptual model of the data required for an information system. There are three types of data models - conceptual, logical, and physical. A conceptual data model describes what the system contains, a logical model describes how the system will be implemented regardless of the database, and a physical model describes the implementation using a specific database. Common elements of a data model include entities, attributes, and relationships. Data modeling is used to standardize and communicate an organization's data requirements and establish business rules.
The document outlines a 7-step process for mapping an entity-relationship (ER) schema to a relational database schema. The steps include mapping regular and weak entity types, binary 1:1, 1:N, and M:N relationship types, multivalued attributes, and n-ary relationship types to tables. For each type of schema element, the document describes how to represent it as a table with primary keys and foreign key attributes that preserve the relationships in the original ER schema.
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
The document discusses the drawbacks of using file systems to manage large amounts of shared data, such as data redundancy, inconsistency, isolation, and lack of security and crash recovery. It then introduces database management systems (DBMS) as an alternative that offers advantages like data independence, efficient access, integrity, security, concurrent access, administration, and reduced application development time. However, DBMS also have disadvantages including cost, size, complexity, and higher impact of failure.
The document outlines the steps for mapping an ER or EER model to a relational database schema. It discusses:
1. The 7 steps for mapping entity types, relationship types, attributes, and other constructs from an ER model to relations. This includes mapping entities, relationships, attributes, specializations/generalizations.
2. Additional steps 8 and 9 for mapping special constructs from an EER model like specialization/generalization and categories/union types. Various options for mapping these constructs are presented.
3. Examples are provided throughout to illustrate how each modeling construct in sample ER/EER diagrams would be mapped to relations and keys following the outlined steps. Figures show both the ER/EER
This document describes the three level architecture of a database management system (DBMS): the external, conceptual, and internal levels. The external level defines different views of the database for users. The conceptual level defines the logical structure and relationships of the entire database. The internal level defines the physical storage and implementation of the data. The document also discusses logical and physical data independence, which refer to the ability to modify schemas at different levels without affecting higher levels.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
The document discusses entity relationship diagrams and database design. It defines key concepts such as entities, attributes, relationships and cardinalities. Entities can have single-valued or multi-valued attributes. Relationships connect entities and can be one-to-one, one-to-many, many-to-one, or many-to-many. Primary keys uniquely identify entities and foreign keys define relationships between entities. Together these elements form a conceptual model of entities and their relationships within a database.
The document discusses Entity Relationship Diagrams (ERDs) which are used for data modeling. It defines ERD components such as data objects, attributes, and relationships. It describes different types of relationships between entities like one-to-one, one-to-many, and many-to-many relationships. Cardinality and degree of relationships are also explained. Examples of ERD diagrams and benefits like effective communication are provided.
The document provides information on entity relationship diagrams (ERDs), including the objectives, components, and steps to create an ERD. It defines key ERD concepts like entities, attributes, relationships, and cardinality. It describes the entity modeling process and discusses how to recognize entities, attributes, relationships, and cardinalities in a database. It outlines the general steps to create an ERD, including identifying entities, finding relationships between entities, drawing a rough ERD, defining primary keys, identifying attributes, mapping attributes to entities, and drawing a fully attributed ERD. Sample ERDs are provided to illustrate concepts like cardinality constraints.
The document discusses concepts related to entity-relationship modeling and database design. It covers:
1. Key concepts in entity-relationship modeling like entities, attributes, relationships and keys.
2. Different types of attributes, relationships and keys.
3. Storage concepts like primary and secondary storage, buffering, and placing records on disks.
4. File organization techniques like hashing, B-trees and file operations.
This document presents a library management system project by six students guided by Kazi Wasif Ahmed. It discusses the existing manual system and proposes an online system with features like online book searching and reservations, barcode scanning for book issues/returns, automatic late fee calculation, and ability for librarians to add new books to the database. Entity relationship diagrams are presented for both the existing and proposed systems along with use case, data flow, and class diagrams. The conclusion states that the project aims to computerize library operations for easier and effective information storage about books and users.
This document discusses entity relationship (ER) diagrams and provides examples. It introduces key concepts for ER diagrams like entities, attributes, key attributes, composite attributes, multi-valued attributes, derived attributes, and relationship types. Examples are given for an employee entity including its attributes and relationships. An ER diagram is also shown for a banking system to illustrate entities, relationships and cardinalities.
The document describes two database design exercises. The first involves designing a database to store information about employees, departments, and employee children. It involves entities for employees, departments, and children and relationships indicating employees work in departments, departments have managers, and children can be identified through their parent employee.
The second exercise involves designing a database for an art gallery. It involves entities for artists, artworks, groups, and customers. Artworks are created by artists and can belong to multiple groups. Customers like certain artists and groups. The solution diagrams the entities and relationships along with constraints like each artwork is created by one artist.
The document provides an overview of entity relationship diagrams (ERDs) including their basic components, different notations, and how to implement various relationship types in a relational database. ERDs depict entities, attributes, and relationships in a conceptual database design. Key points covered include the three main notations of ERDs, solving multi-valued attributes and many-to-many relationships, and how to implement one-to-one, one-to-many, and many-to-many relationships through primary and foreign key constraints.
This documentation have all the details about school management system, even in this document have DFD,ERD,FDD digram that are useful to create database. to get more details about this product plz mail me on (aki_string@yahoo.co.in) thanks.....
ER DIAGRAM TO RELATIONAL SCHEMA MAPPING ARADHYAYANA
1) Entity types from ER diagrams are converted to tables, with attributes becoming columns and the entity's key becoming the primary key. Multi-valued attributes become separate tables linked by foreign keys.
2) Weak entities become tables with a composite primary key of the strong entity's primary key and the weak entity's key.
3) Relationships are represented by either adding foreign keys between tables or creating a separate table for many-to-many relationships containing foreign keys from the related tables.
This document discusses various PHP functions for manipulating dates and times - getdate(), strtotime(), and date().
Getdate() returns an associative array of date/time values for a given timestamp. Strtotime() parses an English textual datetime into a Unix timestamp. Date() formats a timestamp based on a format string, with different format specifiers for dates, times, years, months, and more. Examples are provided for using these functions.
The document discusses entity-relationship (ER) modeling concepts including entities, attributes, relationships, and ER diagrams. It provides an example database for a company (COMPANY) that tracks departments, projects, employees, and employee dependents. Key concepts covered include entity types, relationship types, attributes, keys, weak entities, roles, recursive relationships, and higher order relationships. The example ER diagram for the COMPANY database includes entity types for EMPLOYEE, DEPARTMENT, PROJECT, and DEPENDENT connected by relationship types like WORKS_FOR, MANAGES, WORKS_ON, and DEPENDENTS_OF.
An entity-relationship diagram (ERD) is a graphical representation that depicts the entities and relationships within an information system. An ERD shows a database's entities and the relationships between entities in a symbolic, visual way. It documents a project, clarifies features, and provides a basis for development options. Key components of an ERD include entities, relationships, attributes, and cardinality. The steps to create an ERD are to identify entities, determine interactions, analyze the nature of interactions, and draw the diagram. A good ERD model is simple, non-redundant, and flexible to adapt to future needs.
The document provides requirements for creating an entity relationship diagram (ERD) for a National Hockey League database. It specifies that the ERD should include entities for teams, players, games and their attributes and relationships. A sample ERD is provided that models teams as having a name, city, coach and captain, players as belonging to teams and having attributes, and games as connecting two teams with a date and score.
Entity relationship diagrams show relationships between tables using lines and cardinality notation. Cardinality describes whether a relationship is one-to-one, one-to-many, many-to-one, or many-to-many. Many-to-many relationships cause data problems and are resolved using a link table to create two one-to-many relationships. For example, a books lending relationship between lenders and books would be resolved using a lend records link table.
The document describes setting up PostgreSQL database tables to store flight, fare, and seat data. It creates tables with the appropriate columns and constraints, and populates the flight table with randomly generated number, airline, and weekday values. It then performs sample queries on the flight table.
The document discusses entity relationship diagrams and their components. An entity represents a category of data, and an attribute contains a subgroup of information within an entity. Relationship types in ER diagrams include mandatory, optional, many-to-one, one-to-many, and recursive. Many-to-many relationships should be avoided by dividing them into two one-to-many relationships using a joining table. Examples of different relationship types and resolving many-to-many relationships are provided.
Vanson Bourne Data Summary: Shadow IT - BDMsVanson Bourne
Shadow IT - the commissioning and sourcing of core IT products and services by departments other than IT - has been used across organisations for some time, but it is only now that IT decision makers are beginning to fully appreciate the extent of it. It is a way of working that is increasingly becoming the norm for business departments, who are independently deploying solutions with increasing frequency, despite the risks that may be involved.
IT departments must now adapt if they want to retain some element of control over the way technology is utilised within their organisations. We surveyed 200 business decision makers in organisations with more than 1,000 employees, in both the UK and the US.
This document summarizes discrete random variables. It defines a discrete random variable as having countable and finite possible values. It discusses the probability mass function and cumulative distribution function of discrete random variables. It provides examples of calculating the expected value, variance, and standard deviation of a discrete random variable based on its probability mass function. The document concludes that discrete random variables are an important concept in engineering applications.
1. introduction
2. data
3. dbms
4.uses of dbms
5. purpose of dbms
6. types of dbms
7. er-model
8. how to build a dbms
9.description of table in dbms
10. terminology in a dbms
11. advantages
12. disadvantages
The document discusses concepts related to conceptual database design and the entity-relationship (ER) model. It defines key concepts such as entities, attributes, entity types, entity sets, relationship sets, and ER diagram notations. It provides examples of strong and weak entities. It also explains different types of relationships, cardinality ratios, and participation constraints that can exist between entity sets in an ER diagram.
Entity Relationship Diagram – ER Diagram in DBMS.pptxsukrithlal008
An Entity-Relationship (ER) diagram is a design or blueprint of a database that describes the structure of a database using a diagram. The main components of an ER diagram are entities, attributes, and relationships. An ER diagram shows the relationships among entity sets where an entity set is a group of similar entities that can have attributes. The diagram represents the complete logical structure of a database.
The document discusses data modeling and the entity-relationship model. It defines key concepts like entities, attributes, relationships, and cardinalities. Entities have attributes and can be connected through relationships. Relationships can be one-to-one, one-to-many, many-to-one, or many-to-many depending on how many entities can be associated with each other. The entity-relationship model is useful for conceptual database design and represents these concepts visually in diagrams.
The document discusses the entity-relationship (E-R) model for data modeling. Some key points:
- An entity is an object that exists and is distinguishable from other objects, with attributes that describe it. An entity set is a collection of similar entities.
- Relationships associate entities and are represented as diamonds in E-R diagrams. Cardinality constraints specify the number of entities that can participate in a relationship.
- Primary keys uniquely identify entities and foreign keys in one table reference the primary key of another table. Generalization allows entities to be grouped into supertypes and subtypes.
The document discusses the Entity Relationship (ER) model, which was introduced in 1976 to define the conceptual view of a database. The ER model represents real-world entities and relationships between entities using entity sets, attributes, and relationship types. These constructs allow the ER model to map to a relational database schema and serve as a design tool for database developers and a communication tool for users. Key ER modeling concepts covered include entities, attributes, relationships, cardinalities, and participation constraints.
This document provides an overview of entity-relationship (ER) modeling concepts for conceptual database design. It defines entities as objects with attributes, and relationships as connections between entities. Entities can have simple or composite attributes. Relationships have names, participating entity sets, degrees, and mapping cardinalities like one-to-one, one-to-many, and many-to-many. ER diagrams use shapes and lines to represent these components and show cardinalities. The document also discusses recursive relationships, participation constraints, and examples of an ER diagram for products and suppliers.
This document provides an overview of entity relationship (ER) diagrams and their components. It describes ER diagrams as a way to represent the logical structure of a database using entities, attributes, and relationships. It then gives examples of different types of entities, attributes, and relationships that can be depicted in an ER diagram including weak entities, single-valued and multi-valued attributes, and one-to-one, one-to-many, many-to-one, and many-to-many relationships. Specific ER diagrams are presented for a student management system and hospital management system to further illustrate these concepts.
ER diagrams are used to visually represent the logical structure of databases and the relationships between entities stored in a database. The key components of an ER diagram include entities represented by rectangles, attributes represented by ovals, and relationships represented by diamonds. ER diagrams help identify the entities, attributes, relationships, and cardinalities that should exist in a database design. Creating an ER diagram is an important first step before implementing a relational database.
The document discusses the entity-relationship (ER) model and relational data model for database design. It provides information on the key components of the ER model, including entities, attributes, relationships, keys, and cardinalities. It also describes how the ER model defines the conceptual view of a database using real-world entities and relationships. Additionally, it covers the basics of the relational data model, which uses tables, rows, columns, keys, and relationships between tables to organize data.
Fundamentals of database system - Data Modeling Using the Entity-Relationshi...Mustafa Kamel Mohammadi
In this chapter you will learn
Relational data model concepts
What is entity?
What is attribute and it’s types
What is relationship?
What is an Entity-Relationship data model?
Relational data model constraints
Characteristics of relation
The document discusses data modeling and the entity-relationship (ER) model. It defines key concepts in data modeling like entities, attributes, relationships and keys. It explains how ER diagrams depict entities as objects and relationships between entities. The document also covers extended ER modeling concepts like weak entities, generalization and aggregation.
The document discusses the key concepts of an entity-relationship (E-R) diagram including entities, attributes, and their types. An entity is a piece of data like a student or teacher that is represented in a database. Entities can be strong or weak depending on whether they have a primary key to uniquely identify them. Attributes are the properties of entities like name or age. Attributes can be simple, composite, derived, single-valued, or multi-valued. The E-R diagram provides a graphical representation of how data is organized in a database by showing the relationships between entities and their attributes.
Sree Dattha Institute of Engineering and Sciencedrprkr74
The document defines key characteristics of relations and relationships in a database management system. Relations have unique rows, atomic values, attribute names, and no inherent ordering or duplicate rows. A relationship associates multiple entities, like students being enrolled in courses. Relationships have an arity specifying the number of participating relations, and a degree referring to the number of relationship attributes. Cardinality defines the number of instances associated with each entity in the relationship. Additional ER modeling concepts include generalization, specialization, and aggregation.
Entity Relationship modeling is used to define relationships between entities in a database. It involves creating Entity Relationship Diagrams which use entities, attributes, and relationships to represent how data is connected. The ER diagram defines the entities, their attributes, and the relationships between entities. This modeling helps with database design and implementation by illustrating how data is structured and related.
The document discusses the Entity-Relationship (ER) model, which is a high-level data model used to define data elements and relationships for a specified system. The ER model develops a conceptual design for the database and provides a simple view of data. Key components of the ER model include entities, attributes, relationships, cardinality, and notations used in ER diagrams. Advantages of the ER model are that it is conceptually simple, provides better visual representation, acts as an effective communication tool, and can be easily converted to other data models like the relational model.
1) The document discusses the relational model and database concepts including relations, attributes, tuples, domains, and schemas.
2) It also describes the entity-relationship model which represents data using entities, attributes, and relationships between entities.
3) The key aspects of the entity-relationship model are explained such as entity sets, relationship sets, attributes, keys, and the different types of relationships between entities.
Post init hook in the odoo 17 ERP ModuleCeline George
In Odoo, hooks are functions that are presented as a string in the __init__ file of a module. They are the functions that can execute before and after the existing code.
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2. To be discussed:-
Entity
Attribute
Types of Attribute
Relationship
ER Diagram Representation
Generalization
Specialization
Inheritance
Codd’s 12 Rule
Relational Data Model
ER Model to Relational Model
3. Entity
An entity can be a real-world object, either
animate or inanimate, that can be easily
identifiable. Example : School Database
Teachers
Students
Classes
Courses offered
Entity set
An entity set is a collection of similar types of
entities. An entity set may contain entities
with attribute sharing similar values.
4. Attributes
Entities are represented by means of their properties,
called attributes. All attributes have
values. Example : Student :- Name, Class, Age
Types of Attributes
Simple attribute − Simple attributes are atomic
values, which cannot be divided further. For
example, a student's phone number is an atomic
value of 10 digits.
Composite attribute − example, a student's
complete name may have first_name and
last_name.
5. Types of Attributes Continue…..
Derived attribute − example, average_salary in a
department should not be saved directly in the database,
instead it can be derived.
For another example, age can be derived from
data_of_birth.
Single-value attribute − Single-value attributes contain
single value. example −: Social_Security_Number.
Multi-value attribute − Multi-value attributes may contain
more than one values.
Example: a person can have more than one phone
number, email_address, etc.
6. Relationship
The association among entities is called
relationship.
Ex: Employee works_at department
Ex: Student enrolls in a course
Relationship Set
A set of relationships of similar type is called a
relationship set. Like entities, a relationship too
can have attributes. These attributes are
called descriptive attributes.
7. Mapping cardinalities
Cardinality defines the number of entities in
one entity set, which can be associated with
the number of entities of other set via
relationship set.
One to one
One to many
Many to one
Many to many
14. ER Diagram Representation Continue…
Multivalued
Studen
t
Nam
e
Roll
Birth_Dat
e
Last
First
Phone_No
15. ER Diagram Representation Continue…
Derived
Studen
t
Nam
e
Roll
Birth_Dat
e
Last
First
Phone_No
Age
16. ER Diagram Representation Continue…
Relationship
Binary Relationship and Cardinality
When two entities participate in a relationship then it
is called Binary Relationship.
Cardinality is the number of instance of an entity from
a relation that can be associated with the relation.
17. ER Diagram Representation Continue…
One to One (1:1)
1 1
Example : Person – Passport
Student – Roll-No
Relationshi
p EntityEntity
18. ER Diagram Representation Continue…
One to Many (1:N)
1 N
Example : Mother- Children
Student - Address
Relationshi
p EntityEntity
19. ER Diagram Representation Continue…
Many to One (N:1)
N 1
Example : Students - Teacher
Relationshi
p EntityEntity
20. ER Diagram Representation Continue…
Many to One (M:N)
M N
Example : - Books - Authors
Relationshi
p EntityEntity
21. ER Diagram Representation Continue…
Participation Constraint
Total participation Partial participation
Relationshi
p EntityEntity
22. The ER Model has the power of expressing
database entities in a conceptual hierarchical
manner. As the hierarchy goes up, it generalizes
the view of entities, and as we go deep in the
hierarchy, it gives us the detail of every entity
included.
Going up in this structure is called Generalization
Reverse is called Specialization
Generalization and Specialization
26. Relational Data Model
Relational data model is the primary data model,
which is used widely around the world for data
storage and processing.
Concepts
Touple
Relation Instance
Relation Schema
Relation key
Attribute domain
28. Key Constraints ….
Key constraints force that −
in a relation with a key attribute, no two tuples can
have identical values for key attributes.
a key attribute can not have NULL values.
Domain constraints
Every attribute is bound to have a specific range of values.
Example : Age can not be negative
Referential integrity constraints
29. Key Constraints ….
Referential integrity constraints
Every relation has some conditions that must hold for
it to be a valid relation. These conditions are
called Relational Integrity Constraints.
I If a relation refers to a key attribute of a different or
same relation, then that key element must exist.
30. ER model to Relational Model
ER model
Entity
Attributes
Mapping Entity
Create table for each entity.
Entity's attributes should become fields of tables with their
respective data types.
Declare primary key.
31. ER model to Relational Model
Mapping Relationship
Create table for a relationship.
Add the primary keys of all participating Entities as
fields of table with their respective data types.
If relationship has any attribute, add each attribute
as field of table.
Declare a primary key composing all the primary
keys of participating entities.
Declare all foreign key constraints.
32. ER model to Relational Model
Mapping Weak entity set
Create table for weak entity set.
Add all its attributes to table as field.
Add the primary key of identifying entity set.
Declare all foreign key constraints.
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