An Introduction,
Entities & Relationships,
Building an Entity-Relationship model,
Attributes and Identifiers,
Cardinality, Degree, Existence of
Relationships
The document discusses entity relationship (ER) modeling and database design. It covers collecting requirements, conceptual design, logical design, and physical design. Key aspects of ER modeling are explained, including entities, attributes, relationships, entity types, keys, and conceptual design. The conceptual design shown models entities such as company, department, employee, and their relationships.
This document presents information on the Entity-Relationship (ER) model for conceptual database design. It discusses key concepts of the ER model including entity sets, relationship sets, attributes, constraints, weak entity sets, and superclass/subclass relationships. It provides examples of how these concepts are represented in an ER diagram and outlines good principles for database design.
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 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 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.
This document discusses entity-relationship (E-R) modeling concepts including entity sets, relationship sets, E-R diagrams, keys, attributes, weak entity sets, specialization, generalization, aggregation, and converting an E-R schema to tables. It provides examples and definitions for each concept in 2-3 sentences.
The document discusses entity relationship (ER) modeling concepts including:
- Entities, attributes, and relationships can be represented graphically in ER diagrams
- Relationships have cardinalities like one-to-one, one-to-many, many-to-many that specify how entities are associated
- Weak entities depend on other entities and cannot be uniquely identified without attributes from the associated entity
The document discusses entity relationship (ER) modeling and database design. It covers collecting requirements, conceptual design, logical design, and physical design. Key aspects of ER modeling are explained, including entities, attributes, relationships, entity types, keys, and conceptual design. The conceptual design shown models entities such as company, department, employee, and their relationships.
This document presents information on the Entity-Relationship (ER) model for conceptual database design. It discusses key concepts of the ER model including entity sets, relationship sets, attributes, constraints, weak entity sets, and superclass/subclass relationships. It provides examples of how these concepts are represented in an ER diagram and outlines good principles for database design.
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 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 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.
This document discusses entity-relationship (E-R) modeling concepts including entity sets, relationship sets, E-R diagrams, keys, attributes, weak entity sets, specialization, generalization, aggregation, and converting an E-R schema to tables. It provides examples and definitions for each concept in 2-3 sentences.
The document discusses entity relationship (ER) modeling concepts including:
- Entities, attributes, and relationships can be represented graphically in ER diagrams
- Relationships have cardinalities like one-to-one, one-to-many, many-to-many that specify how entities are associated
- Weak entities depend on other entities and cannot be uniquely identified without attributes from the associated entity
The document discusses the entity-relationship (ER) model for database design. It describes the basic constructs of ER modeling including entities, attributes, relationships, and cardinality. Entities can have attributes and exist in entity sets. Relationships associate entities and have properties like degree, connectivity, and cardinality. The ER diagram uses graphical notation to represent these constructs including rectangles for entities and lines or diamonds for relationships. The document provides examples of modeling customers, loans, and university course enrollment using the ER model.
The document discusses enhanced entity-relationship (EER) modeling concepts used to more completely represent requirements of complex database applications. It introduces subclasses/superclasses to represent subgroupings of entities, with subclasses inheriting attributes and relationships from superclasses. Specialization defines subclasses of a superclass based on distinguishing characteristics, while generalization combines entity sets with common features into a higher-level superclass. Constraints on specialization/generalization include predicate-defined subclasses with membership conditions and attribute-defined specializations.
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 provides an overview of conceptual database design using the Entity-Relationship (ER) model. It describes the basic constructs of the ER model including entities, relationships, attributes, and additional features like weak entities, inheritance hierarchies, and aggregation. It also discusses modeling choices like representing concepts as entities or attributes, binary vs n-ary relationships. Constraints that can be expressed in the ER model are covered, along with the subjective nature of ER design.
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.
Data modeling using the entity relationship modelJafar Nesargi
The document describes key concepts in entity relationship modeling including entity types, attributes, relationships, keys, and constraints. It provides an example database application to track employees, departments, and projects within a company. It then defines entity types for departments, projects, employees, and dependents with their attributes. It also describes relationship types, cardinalities, roles, and other modeling constructs used to design the conceptual schema.
The document provides an overview of entity-relationship (ER) modeling concepts including entities, attributes, relationships, and ER diagram notation. It then presents an example database application for a company (COMPANY) and defines the entity types, relationship types, and attributes that would be included in an ER diagram for the COMPANY database schema. Key concepts covered include entities, attributes, relationships, cardinalities, participation constraints, and weak entity types.
The document discusses how to model a database using an entity-relationship (ER) model. It describes the key components of an ER model including entities, attributes, relationships, and keys. It explains how entities can have attributes and how relationships associate entities. It also covers mapping cardinalities, weak entities, specialization/generalization, and how to map an ER diagram to relational database tables.
Database Systems - Entity Relationship Modeling (Chapter 4/2)Vidyasagar Mundroy
This document introduces entity-relationship modeling, which is a popular way to design databases. It discusses key concepts such as entities, attributes, relationships, and structural constraints. Entities represent real-world objects, and attributes describe their characteristics. Relationships associate entities, and come in different degrees (binary, ternary). Structural constraints specify how many entities can participate in a relationship, such as one-to-one, one-to-many, or many-to-many relationships. Together, entities, attributes, relationships and constraints form the basis of entity-relationship modeling.
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.
This document discusses entity relationship (ER) modeling. It defines key concepts in ER modeling including entities, attributes, relationships, and ER diagram notations. Entities can be people, places, objects or concepts and are grouped into entity types. Attributes provide information about entities. Relationships define how entities are connected. Common relationship types are one-to-one, one-to-many, many-to-one, and many-to-many. ER diagrams use notations like boxes, lines, and crow's foot symbols to visually depict entities, attributes, and relationships in a database design. The document also covers entity classification, primary keys, foreign keys, and potential problems in ER modeling.
The document discusses the Entity-Relationship (ER) model used for conceptual database design. The ER model uses entities, attributes, and relationships to model real-world concepts and connections. The model allows expression of key constraints, participation constraints, and other integrity rules. Conceptual design requires determining the best way to represent concepts as entities or attributes and relationships.
The document discusses key concepts in entity relationship diagrams including entities, attributes, entity sets, relationships, relationship sets, cardinality, roles, and identifiers. It defines entities as people, places, objects or events and describes their attributes. It also covers the different types of relationships that can exist between entities like one-to-one, one-to-many, many-to-one, and many-to-many. Finally, it discusses primary keys, foreign keys, and the importance of identifying keys for looking up and linking information between tables.
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.
In software engineering, an entity–relationship model (ER model) is a data model for describing the data or information aspects of a business domain or its process requirements
The document provides an overview of entity-relationship (ER) modeling concepts including:
- Entity sets represent collections of real-world objects or concepts that share common properties
- Relationship sets represent associations between entity sets
- ER diagrams use graphical symbols like rectangles, diamonds, and lines to represent entity sets, relationship sets, and attributes
- Mapping cardinalities define the number of entities in one set that can be associated with entities in another set through a relationship
- Keys uniquely identify entities and relationships and primary keys are selected from candidate keys
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.
The document discusses key concepts in entity-relationship modeling for database design including:
1. Entities should represent real-world objects like people, places, things while avoiding outputs or users.
2. Relationships define connections between entities and can have attributes. Cardinality constraints specify how many entities can connect.
3. Weak entities rely on strong entities for their identifiers. Associative entities act as both relationships and entities.
The document discusses the MySQL database design process. It covers defining keys to uniquely identify records, the different types of table relationships including one-to-one, one-to-many, and many-to-many, and the process of normalization to minimize duplication and inconsistencies. The three main levels of normalization discussed are first, second, and third normal forms, each with their own rules. Following proper database design, normalization, and defining relationships between tables is important for ensuring an efficient, flexible, and maintainable database structure.
The document discusses relational database design and normalization. It covers first normal form, functional dependencies, and decomposition. The goal of normalization is to avoid data redundancy and anomalies. First normal form requires attributes to be atomic. Functional dependencies specify relationships between attributes that must be preserved. Decomposition breaks relations into smaller relations while maintaining lossless join properties. Higher normal forms like Boyce-Codd normal form and third normal form further reduce redundancy.
The document discusses the entity-relationship (ER) model for database design. It describes the basic constructs of ER modeling including entities, attributes, relationships, and cardinality. Entities can have attributes and exist in entity sets. Relationships associate entities and have properties like degree, connectivity, and cardinality. The ER diagram uses graphical notation to represent these constructs including rectangles for entities and lines or diamonds for relationships. The document provides examples of modeling customers, loans, and university course enrollment using the ER model.
The document discusses enhanced entity-relationship (EER) modeling concepts used to more completely represent requirements of complex database applications. It introduces subclasses/superclasses to represent subgroupings of entities, with subclasses inheriting attributes and relationships from superclasses. Specialization defines subclasses of a superclass based on distinguishing characteristics, while generalization combines entity sets with common features into a higher-level superclass. Constraints on specialization/generalization include predicate-defined subclasses with membership conditions and attribute-defined specializations.
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 provides an overview of conceptual database design using the Entity-Relationship (ER) model. It describes the basic constructs of the ER model including entities, relationships, attributes, and additional features like weak entities, inheritance hierarchies, and aggregation. It also discusses modeling choices like representing concepts as entities or attributes, binary vs n-ary relationships. Constraints that can be expressed in the ER model are covered, along with the subjective nature of ER design.
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.
Data modeling using the entity relationship modelJafar Nesargi
The document describes key concepts in entity relationship modeling including entity types, attributes, relationships, keys, and constraints. It provides an example database application to track employees, departments, and projects within a company. It then defines entity types for departments, projects, employees, and dependents with their attributes. It also describes relationship types, cardinalities, roles, and other modeling constructs used to design the conceptual schema.
The document provides an overview of entity-relationship (ER) modeling concepts including entities, attributes, relationships, and ER diagram notation. It then presents an example database application for a company (COMPANY) and defines the entity types, relationship types, and attributes that would be included in an ER diagram for the COMPANY database schema. Key concepts covered include entities, attributes, relationships, cardinalities, participation constraints, and weak entity types.
The document discusses how to model a database using an entity-relationship (ER) model. It describes the key components of an ER model including entities, attributes, relationships, and keys. It explains how entities can have attributes and how relationships associate entities. It also covers mapping cardinalities, weak entities, specialization/generalization, and how to map an ER diagram to relational database tables.
Database Systems - Entity Relationship Modeling (Chapter 4/2)Vidyasagar Mundroy
This document introduces entity-relationship modeling, which is a popular way to design databases. It discusses key concepts such as entities, attributes, relationships, and structural constraints. Entities represent real-world objects, and attributes describe their characteristics. Relationships associate entities, and come in different degrees (binary, ternary). Structural constraints specify how many entities can participate in a relationship, such as one-to-one, one-to-many, or many-to-many relationships. Together, entities, attributes, relationships and constraints form the basis of entity-relationship modeling.
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.
This document discusses entity relationship (ER) modeling. It defines key concepts in ER modeling including entities, attributes, relationships, and ER diagram notations. Entities can be people, places, objects or concepts and are grouped into entity types. Attributes provide information about entities. Relationships define how entities are connected. Common relationship types are one-to-one, one-to-many, many-to-one, and many-to-many. ER diagrams use notations like boxes, lines, and crow's foot symbols to visually depict entities, attributes, and relationships in a database design. The document also covers entity classification, primary keys, foreign keys, and potential problems in ER modeling.
The document discusses the Entity-Relationship (ER) model used for conceptual database design. The ER model uses entities, attributes, and relationships to model real-world concepts and connections. The model allows expression of key constraints, participation constraints, and other integrity rules. Conceptual design requires determining the best way to represent concepts as entities or attributes and relationships.
The document discusses key concepts in entity relationship diagrams including entities, attributes, entity sets, relationships, relationship sets, cardinality, roles, and identifiers. It defines entities as people, places, objects or events and describes their attributes. It also covers the different types of relationships that can exist between entities like one-to-one, one-to-many, many-to-one, and many-to-many. Finally, it discusses primary keys, foreign keys, and the importance of identifying keys for looking up and linking information between tables.
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.
In software engineering, an entity–relationship model (ER model) is a data model for describing the data or information aspects of a business domain or its process requirements
The document provides an overview of entity-relationship (ER) modeling concepts including:
- Entity sets represent collections of real-world objects or concepts that share common properties
- Relationship sets represent associations between entity sets
- ER diagrams use graphical symbols like rectangles, diamonds, and lines to represent entity sets, relationship sets, and attributes
- Mapping cardinalities define the number of entities in one set that can be associated with entities in another set through a relationship
- Keys uniquely identify entities and relationships and primary keys are selected from candidate keys
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.
The document discusses key concepts in entity-relationship modeling for database design including:
1. Entities should represent real-world objects like people, places, things while avoiding outputs or users.
2. Relationships define connections between entities and can have attributes. Cardinality constraints specify how many entities can connect.
3. Weak entities rely on strong entities for their identifiers. Associative entities act as both relationships and entities.
The document discusses the MySQL database design process. It covers defining keys to uniquely identify records, the different types of table relationships including one-to-one, one-to-many, and many-to-many, and the process of normalization to minimize duplication and inconsistencies. The three main levels of normalization discussed are first, second, and third normal forms, each with their own rules. Following proper database design, normalization, and defining relationships between tables is important for ensuring an efficient, flexible, and maintainable database structure.
The document discusses relational database design and normalization. It covers first normal form, functional dependencies, and decomposition. The goal of normalization is to avoid data redundancy and anomalies. First normal form requires attributes to be atomic. Functional dependencies specify relationships between attributes that must be preserved. Decomposition breaks relations into smaller relations while maintaining lossless join properties. Higher normal forms like Boyce-Codd normal form and third normal form further reduce redundancy.
Standard language for querying and manipulating data
Structured Query Language
Many standards out there:
- ANSI SQL, SQL92 (a.k.a. SQL2), SQL99 (a.k.a. SQL3), ....
- Vendors support various subsets: watch for fun discussions in class !
An Introduction To Software Development - Architecture & Detailed DesignBlue Elephant Consulting
This document discusses software design and architecture. It covers the architectural design phase, which provides the highest-level overview of a system's main components and relationships between components. The detailed design phase then decomposes components into finer detail. The document also discusses software architectural styles like MVC and layered architectures. It explains how to do a functional decomposition and modular decomposition for detailed design. Finally, it covers relational database design and the four phases of representing and storing data in a database efficiently.
The document discusses conceptual data modeling and entity-relationship (ER) modeling. It describes the key concepts in ER modeling including entities, attributes, relationships, cardinality, participation, and relationship types. It provides examples of how to model different types of relationships, attributes, and entities. The goal of conceptual modeling is to build an abstract yet rigorous model of an organization's data to help communicate requirements and ensure quality.
The document discusses database design process which can be broken down into 5 phases - planning, analysis, design, implementation and maintenance. It describes the conceptual, logical and physical data models. The conceptual model involves entities, attributes and relationships. The logical model maps the conceptual model to tables, fields, primary and foreign keys. The physical model deals with data storage and access. The document also covers entity relationship diagrams, normalization forms and tips for effective ER diagrams.
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
Functional dependencies play a key role in database design and normalization. A functional dependency (FD) is a constraint that one attribute determines another. FDs have various definitions but generally mean that given the value of one attribute (left side), the value of another attribute (right side) is determined. Armstrong's axioms are used to derive implied FDs from a set of FDs. The closure of an attribute set or set of FDs finds all attributes/FDs logically implied. Normalization aims to eliminate anomalies and is assessed using normal forms like 1NF, 2NF, 3NF, BCNF which impose additional constraints on table designs.
This document discusses database design and the systems development life cycle (SDLC). It explains that the SDLC traces the history of an information system through planning, analysis, design, implementation, and maintenance phases. Within the information system, the database life cycle (DBLC) describes the history of the database through initial study, design, implementation, testing, operation, and maintenance/evolution phases. The chapter also covers conceptual database design strategies like top-down vs. bottom-up and centralized vs. decentralized design.
Normalization is the process of organizing data in a database. This includes creating tables and establishing relationships between those tables according to rules designed both to protect the data and to make the database more flexible by eliminating redundancy and inconsistent dependency.
The document discusses database design and the design process. It explains that database design involves determining the relationships between data elements, structuring them logically, and specifying attributes. The design process includes determining the database purpose, finding and organizing information, dividing it into tables, specifying columns and primary keys, and setting up relationships. It also recommends creating child tables to store multiple pieces of information rather than adding many fields to the core table. This improves the database structure and makes it more extensible.
Presentation of research findings into the provision of course in Computer Science in upper second level education internationally at the NCCA Seminar on the introduction Computer Science in the Leaving Certificate. Dublin Castle 21st February 2017.
Research project led by Neil Keane & Clare McInerney of the Irish Software Research Centre.
Supported by an expert research group of Prof. Kevin Ryan, Prof. Tiziana Margaria, Prof. Rory O’Connor, Dr. Chris Exton (from Lero), Dr. Oliver McGarr, Prof. Sibel Erduran (from National STEM Centre at the School of Education University of Limerick)and Mr. Ted Parslow (Third Level Computing Forum).
Functional dependencies and normalization for relational databasesJafar Nesargi
This document discusses guidelines for designing relational databases. It covers four informal measures of quality: semantics of attributes, reducing redundancy, reducing null values, and avoiding spurious tuples. The guidelines are: 1) design relations so their meaning is clear, 2) avoid anomalies like insertion, deletion and modification anomalies, 3) minimize null values in attributes, and 4) design relations to join without generating spurious tuples. The document uses examples to illustrate these concepts and their importance for database design.
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Hair 9/1/2013
Name Phone Address DOB
Anna 215-123-4567 123 City
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8/14/1995
Nathan 267-333-4444 999 Oak
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second iteration: find every appointment with an Appt Date of 6/1/1998
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The document discusses the process of designing a database management system (DBMS). It describes the six phases of the database lifecycle: initial study, design, implementation, testing and evaluation, operation, and maintenance and evaluation. The design phase is the most critical, as it ensures the final system meets user requirements through conceptual, logical, and physical database design. Successful DBMS design requires balancing the needs of users, infrastructure, and management.
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.
This document provides an overview of key database concepts, including:
- Types of databases and database management systems (DBMS) functions
- Data models like relational, hierarchical, and object-oriented
- The three-schema architecture with conceptual, internal, and external schemas
- Languages used to define and manipulate database structures and data
- Centralized and client-server database system architectures
This document provides an introduction and overview of databases and the basic operations used to manage data in a database using Microsoft Access 2007. It defines what a database is, how data is organized in tables with rows and columns, and when it is appropriate to use a database. It also outlines and provides examples of the basic CRUD (create, read, update, delete) operations used in structured query language (SQL) to manipulate data, including inserting, selecting, updating, and deleting records from database tables.
The document discusses the entity-relationship model, which is a top-down design approach for database design that begins by identifying important data entities and relationships between data. It describes key concepts of the model including entity types, relationship types, attributes, keys, and structural constraints like multiplicity. Examples are provided to illustrate entity-relationship diagrams and different types of relationships like one-to-one, one-to-many, and many-to-many.
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 discusses relational database concepts including:
1) A relational database represents data using relations (tables) with rows and columns, where each row represents a fact and each column represents an attribute.
2) Entity-relationship (ER) modeling represents real-world entities and relationships between entities using entity types, relationship types, and attributes.
3) Relationship types associate multiple entity types and have structural constraints like cardinality ratios that specify the minimum and maximum number of relationships an entity can participate in.
The document discusses relational database concepts including:
1) A relational database represents data using relations (tables) with rows and columns, where each row represents a fact and each column represents an attribute.
2) Entity-relationship (ER) modeling represents real-world entities and relationships between entities using entity types, relationship types, and attributes.
3) Relationship types associate multiple entity types and have structural constraints like cardinality ratios that specify the minimum and maximum number of relationships an entity can participate in.
The document contains information about entity-relationship (ER) modeling including:
1. It discusses the key components of an ER model including entities, attributes, relationships, and cardinality.
2. It provides examples of one-to-one, one-to-many, and many-to-many relationships between entities.
3. It describes the different types of attributes such as simple, composite, single-valued, multi-valued, and derived attributes.
The document introduces database management systems and conceptual database design using the Entity Relationship model. It discusses key concepts such as the universe of discourse, requirements analysis, conceptual schema, entity types, attributes, relationships and constraints. The conceptual schema provides a high-level design of the database that is independent of specific DBMS implementations and easy for non-technical users to understand. This conceptual design forms the basis for later logical and physical database design steps.
An entity-relationship diagram (ERD) is a data modeling technique used to graphically represent the relationships between entities in a database. The key components of an ERD include entities, attributes, and relationships. Entities represent real-world objects, attributes describe entities, and relationships define interactions between entities. To create an ERD, the first steps are identifying entities, determining relationships between entities, analyzing relationship cardinality, and drawing the diagram. The ERD can then be converted to a relational database by creating tables for each entity and relationship with columns for each attribute.
The document provides information on entity relationship diagrams and database architecture. It discusses the three levels of a database architecture: external, conceptual, and internal. It then covers key concepts in ER modeling including entities, attributes, relationships, cardinalities, participation constraints, weak and strong entities, and how to construct an ER diagram.
The document discusses database design concepts including the entity-relationship model, normalization, and functional dependencies. It provides descriptions of key concepts such as entities, attributes, relationships, and mapping approaches from ER diagrams to relational schemas. It also covers normal forms up to BCNF and the goals of normalization to reduce data redundancy and anomalies.
An entity relationship diagram (ERD) is a graphical representation of an organization's data storage requirements. ERDs identify the data that must be captured and stored to support business activities and performance measures. They have three main components - entities, attributes, and relationships. Entities represent people, places, things, and concepts of interest. Attributes are properties of entities. Relationships define associations between entity types. Together these components provide a model of an organization's data.
The chapter discusses database modeling and covers the following key points:
1) It introduces the entity-relationship model and describes how to construct conceptual data models using entities, attributes, relationships and generalization.
2) It describes logical database design and different logical data models including object-oriented, hierarchical and relational models. Key concepts of the object-oriented model such as objects, classes, encapsulation and inheritance are explained.
3) Examples are provided to illustrate modeling common business situations using both entity-relationship and object-oriented diagrams.
The document provides an overview of the entity-relationship model for database design. It discusses key concepts such as entities, attributes, relationships, and cardinality constraints. It also covers advanced topics like weak entities, generalization/specialization, and aggregation. The entity-relationship model involves representing real-world objects and associations between objects using a graphical diagram to design the database at a conceptual level.
The entity-relationship (E-R) model is a way to graphically represent entities and relationships between entities to create a database. The E-R model employs three basic concepts: entity sets, relationship sets, and attributes. An entity set is a set of entities of the same type that share attributes. Attributes are descriptive properties of each member of an entity set. There are different types of attributes including simple/composite, single/multi-valued, derived, key, and null attributes.
The document discusses the phases of database design including requirements collection and analysis, conceptual design, and relational database schema. It provides examples of collecting requirements for a company database including entity types like department, project, and employee. It also covers conceptual modeling using an entity-relationship diagram to represent entities, attributes, relationships and constraints. Key concepts explained include entity types, attributes, relationships, cardinalities, participation constraints, weak entities, and converting a conceptual schema to a relational database schema.
Requirements of a Conceptual Data Model
Expressiveness: should be expressive enough to allow modeling of different types of relationships, objects and constraints of the miniworld.
Simplicity: non-specialists should be able to understand
Diagrammatic Representation: to ease interpretation
Formality: There should be no ambiguity in the specification
This document discusses data modeling concepts and the process of logical data modeling. It defines key concepts such as entities, attributes, relationships, keys, and normalization. It explains how to develop a logical data model through stages including creating a context data model, key-based data model, and fully attributed data model. The goals of data modeling are to develop a simple, nonredundant, flexible and adaptable data model. Normalization techniques such as 1NF, 2NF and 3NF are used to analyze and improve the data model.
The document provides an overview of the entity-relationship (ER) model for database design. It describes the key components of an ER model including entities, attributes, relationships, keys, and cardinalities. It also discusses common modeling techniques like weak entities, derived attributes, and participation constraints. Examples are provided to illustrate different relationship types, keys, and cardinalities. Common problems with ER modeling like connection traps are also covered. The document concludes with best practices for designing a good ER model.
This document discusses entity-relationship (ER) modeling and ER diagrams. It defines key concepts such as entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also covers ER diagram notation for different types of attributes, keys, roles, and relationship cardinalities. The goal of ER modeling and diagrams is to conceptualize a database without technical details.
The document discusses the basics of conceptual data modeling using the entity-relationship model. It defines the main element types in an ERM including entity types, relationship types, edges, attributes, keys, and generalization/specialization relationships. It provides examples of how entities like students, courses, apartments, kitchens, employees, computers, people, and driving licenses can be modeled in an ERM using different relationship types and cardinalities.
This document summarizes different concurrency control protocols used to manage concurrent transactions in a database system. It describes lock-based protocols that use exclusive and shared locks to control access to data items. It also covers timestamp-based protocols that assign timestamps to transactions to determine serialization order and validation-based protocols that validate transactions in three phases before committing updates. Finally, it discusses deadlock handling where the system detects and recovers from deadlocks by selecting a victim transaction to rollback.
The document discusses various aspects of database recovery systems including failure classification, log-based recovery using redo and rollback approaches, immediate and deferred update schemes, checkpoints, shadow paging, and backup strategies. Recovery approaches are chosen based on the frequency and impact of failures.
The document discusses data normalization and Codd's rules for database design. It covers normal forms including 1NF, 2NF, 3NF, and BCNF. The goals of normalization are to reduce redundancy, ensure data consistency, and avoid anomalies when data is updated, inserted or deleted. Normalization involves separating relations and attributes to eliminate transitive dependencies and potential anomalies. The document provides examples demonstrating how normalization progresses from 1NF to 2NF to 3NF to remove partial and transitive dependencies.
The document discusses database management systems and different data models. It provides an overview of hierarchical, network, relational, object-oriented, and object-relational data models. A database management system uses one of these data models to organize and manage data in a database. It also lists some advantages of using a database approach such as reducing data redundancy and improving data integrity.
The document discusses security and integrity in databases. It covers security topics like authorization, views, encryption and integrity topics like constraints, triggers. For security, it describes how authorization can be granted at different levels and for different operations. Views can restrict access to specific data. Encryption protects against unauthorized access. For integrity, it defines entity constraints, domain constraints and referential integrity to maintain data accuracy. Triggers can automatically enforce rules when data is updated.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
2. Contents
An Introduction
Entities & Relationships
Building an Entity-Relationship model
Attributes and Identifiers
Cardinality, Degree, Existence of
Relationships
3. Life-Cycle
n Requirements
n specification of customer/user needs/desires
n Design
n specification of potential solution or solution
approach
n Implementation
n providing the solution
n Test Results
n evaluations, inferences, reports, documentation
n Modifications
n changes/additions to solution
4. E-R Model (Peter Chen, 1976)
n Diagrammatic
n Simple but expressive
n Easy to map into traditional DBMS
models
n Extensions
n Extended ER Model
n Entity Category Relationship Model
n Enhanced ER Model
5. The Conceptual Model
Conceptual model captures the global/
institutional view of the data semantics.
It investigates and enumerates the various
entities that participate in the business
environment being modelled.
6. E-R Modeling
Entity-Relationship (E-R) Modeling is a
conceptual modeling tool.
perceives the business environment in terms of
participating “entities” and the “relationship”
between them.
e.g. many employees work for a department.
works_
EMPLOYEE DEPARTMENT
for
entity relationship entity
7. Entity
is a “data object”
models some object/entity in the real-world;
entity type represents the set of all similar
objects.
identified by the nouns in the requirements
specification.
must have a name that is unique across the
entire model and has a consistent meaning
across the modelling team and the end users.
8. Attributes
characteristics/properties of an entity, that
provide descriptive details of it.
every attribute must be given a name that is
unique across the entity (distinct entities may
have attributes with the same name).
attribute names are also subject to the same
rules that govern entity names (consistent
meaning, documentation, etc..)
9. Types of Attributes
n Simple and composite
n Single-valued and multivalued
n Null
n Derived
10. Simple and Composite
Attributes
n Simple Attribute: An attribute composed of a
single component with an independent
existence. E.g position and salary of the Staff
entity.
n Composite Attribute: An attribute composed
of multiple components, each with an
independent existence. E.g adress attribute of
the branch entity that can be subdivided into
street, city and postcode attributes.
11. Single-Valued and Multi-
Valued Attributes
n Single-Valued Attribute: An attribute that
holds a single valuefor each occurrence of an
entity type. E.g branchNo.
n Multi-Valued Attributes: An attribute that
holds multiple values for each occurrence of
an entity type. E.g telephoneNo.
12. Derived Attributes
n Derived Attributes: An attribute that
represents a value that is derivable from the
value of a related attribute or set of
attributes, not necessarily in the same entity
type.
n E.g attribute duration which value is
derived from the rentStart and rentFinish
attributes.
13. Relationship
models the real-world association between two
or more entities (binary, n-ary relationship).
A relationship can be optional or mandatory
“degree” is the number of entity sets involved in
the relationship. typically 2 (binary); other
common degrees are 1 (recursive) and 3 (ternary).
14. Relationship:Mapping Cardinality
“Cardinality” indicates the entity
occurrences (instances) participating in a
relationship.
takes values “one” or “many”.
e.g. a one:many relationship indicates that for
every
occurrence of one entity, there are many
related instances of the other entity.
works_
EMPLOYEE DEPARTMENT
for
15. One-to-One (1:1)
Staff Branch
Manages
staffNo 1..1 0..1 branchNo
“Each branch is managed by “A member of staff can
manage zero or one branch”
One member of the staff”
16. One-to-Many (1:*)
Staff PropertyForRent
Oversees
staffNo 0..1 0..* propertyNo
“Each properity for rent is “Each member of staff
overseen by zero or one oversees zero or more
member of staff” properitys for rent”
17. Many-to-Many (*:*)
Newspaper PropertyForRent
Advertises
newspaperName 0..* 1..* propertyNo
“Each properity for rent is “Each newspaper advertises
advertised in zero or more one or more properties for
newspapers” rent”
18. Building the ER Model
the requirements specification is the first step to
any design; it captures the ‘what’ of the business
environment.
also documents the “business rules” - i.e., the
constraints that will apply to your database.
e.g. every department must have a manager;
and only one manager.
the ER model must capture the participating
entities as well as these business rules.
19. Entity : Categorisation
Fundamental/strong entity
an entity that is capable of its “own
existence” - i.e. an entity whose instances
exist notwithstanding the existence of other
entities.
Weak Entities
Associative Entities
20. Entity types : Weak
an entity that is not capable of “its own
existence”.
characterised by the need to have at least
one external identifier (of another entity)
as part of its own identifier.
e.g. consider “ payment” and “ pmt_items”
“ pmt_items” cannot exist without a
corresponding
“ payment” instance. “pmt_id” of “ payment”
will be part of the identifier of “ pmt_items”
21. Entity types : Associative
a relationship translates into migration of a key
- many:many relationship implies the keys
migrating many times, both ways.
such migration leads to redundancy and
many:many relationships must therefore be
resolved.
“Associative entity” is an entity that is used to
resolve a many:many relationship.
22. Summary
Entities & Relationships
Building an Entity-Relationship model
Attributes and Identifiers
Cardinality, Degree, Existence of
Relationships