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The document reviews database concepts like fields, attributes, data types, primary keys and validation rules. It provides examples of designing databases to store student information and sales data. It also discusses database objects like tables, queries, forms and reports. Entity-relationship diagrams are explained as a way to model relationships between entities like one-to-one, one-to-many and many-to-many. Examples are given of modeling relationships for students, forms, employees, projects and more.
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databases3b
This document discusses database concepts like fields, attributes, data types, primary keys and validation rules. It provides examples of designing databases to store student information and sales data. Entity-relationship diagrams are explained to represent relationships between entities like one-to-one, one-to-many and many-to-many. Examples are given of entity-relationship diagrams for a hospital database and police database.
Relational database
The document discusses key concepts of relational databases including:
1. Relational databases organize data into tables with records and fields and allow for defining relationships between tables.
2. Tables represent relations with rows as tuples and columns as attributes.
3. Common operations on relations include select, project, join, union, intersection and difference which allow querying and manipulating the data.
4. The document provides examples of designing database tables to model real-world entities and relationships. Primary keys are used to uniquely identify rows.
Sq lite module3
This document provides an overview of key concepts related to relational database management systems (RDBMS) including:
- It discusses characteristics of relational tables, keys such as primary and foreign keys, and integrity rules.
- It introduces entity relationship diagrams (ERDs) and how they are used to model relationships between entities/tables through attributes and relationships.
- It reviews key terms in the relational model including the differences between logical and physical names, as well as data definition language (DDL) and data manipulation language (DML).
Data model
A collection of conceptual tools for describing
data
data relationships
data semantics
data constraints
Relational model
Entity-Relationship model
Other models:
object-oriented model
semi-structured data models
Older models: network model and hierarchical model
CSPro Workshop P-3
This document provides an overview of creating dictionaries for CSPro data files. It discusses what dictionaries are, their purpose and format. Key points include:
- Dictionaries describe the contents and structure of CSPro data files which are flat, text-based files.
- They end in .dcf and define identification items, levels, records, items, subitems and value sets.
- Identification items uniquely identify each case. Items are the variables for each question.
- Value sets define valid values for items. Special values like not applicable can also be defined.
- Dictionaries should be carefully modified only before or after data entry to avoid errors.
physical and logical data independence
Physical and logical data independence are described. Logical data independence refers to the ability to change the logical schema without impacting external schemas or application programs. Physical data independence is the ability to change the physical schema without affecting the logical schema, such as changing storage structures or devices. Examples of each type of independence are provided. The key elements of an entity-relationship model - entities, attributes, and relationships - are defined. An example E-R diagram showing students, courses, and their relationship is depicted to illustrate these elements.
Database Modeling
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.
Database Systems - Entity Relationship Modeling (Chapter 4/2)
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.
Recommended
databases3b
This document discusses database concepts like fields, attributes, data types, primary keys and validation rules. It provides examples of designing databases to store student information and sales data. Entity-relationship diagrams are explained to represent relationships between entities like one-to-one, one-to-many and many-to-many. Examples are given of entity-relationship diagrams for a hospital database and police database.
Relational database
The document discusses key concepts of relational databases including:
1. Relational databases organize data into tables with records and fields and allow for defining relationships between tables.
2. Tables represent relations with rows as tuples and columns as attributes.
3. Common operations on relations include select, project, join, union, intersection and difference which allow querying and manipulating the data.
4. The document provides examples of designing database tables to model real-world entities and relationships. Primary keys are used to uniquely identify rows.
Sq lite module3
This document provides an overview of key concepts related to relational database management systems (RDBMS) including:
- It discusses characteristics of relational tables, keys such as primary and foreign keys, and integrity rules.
- It introduces entity relationship diagrams (ERDs) and how they are used to model relationships between entities/tables through attributes and relationships.
- It reviews key terms in the relational model including the differences between logical and physical names, as well as data definition language (DDL) and data manipulation language (DML).
Data model
A collection of conceptual tools for describing
data
data relationships
data semantics
data constraints
Relational model
Entity-Relationship model
Other models:
object-oriented model
semi-structured data models
Older models: network model and hierarchical model
CSPro Workshop P-3
This document provides an overview of creating dictionaries for CSPro data files. It discusses what dictionaries are, their purpose and format. Key points include:
- Dictionaries describe the contents and structure of CSPro data files which are flat, text-based files.
- They end in .dcf and define identification items, levels, records, items, subitems and value sets.
- Identification items uniquely identify each case. Items are the variables for each question.
- Value sets define valid values for items. Special values like not applicable can also be defined.
- Dictionaries should be carefully modified only before or after data entry to avoid errors.
physical and logical data independence
Physical and logical data independence are described. Logical data independence refers to the ability to change the logical schema without impacting external schemas or application programs. Physical data independence is the ability to change the physical schema without affecting the logical schema, such as changing storage structures or devices. Examples of each type of independence are provided. The key elements of an entity-relationship model - entities, attributes, and relationships - are defined. An example E-R diagram showing students, courses, and their relationship is depicted to illustrate these elements.
Database Modeling
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.
Database Systems - Entity Relationship Modeling (Chapter 4/2)
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.
INF 103(ASH) Learn/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
INF 103 Week 1 DQ 1 How Do You Currently Use Information Technology
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It is a semantic data model that is used for the graphical representation of the conceptual database design. The semantic data models provide more constructs that is why a database design in a semantic data model can contain/represent more details. With a semantic data model, it becomes easier to design the database, at the first place, and secondly it is easier to understand later. We also know that conceptual database is our first comprehensive design. It is independent of any particular implementation of the database, that is, the conceptual database design expressed in E-R data model can be implemented using any DBMS. For that we will have to transform the conceptual database design from E-R data model to the data model of the particular DBMS. There is no DBMS based on the E-R data model, so we have to transform the conceptual database design anyway.
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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.
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This document introduces data modeling and its key concepts. It defines a data model as a precise description of data in a system, including conceptual, logical, and physical models. Data models are created to develop sound database designs that avoid anomalies. Common data modeling approaches are introduced, including entity-relationship models and UML. The document outlines the process of creating an ER model, which involves identifying entities, attributes, primary keys, and relationships between entities. Relationship cardinality and different attribute types such as composite, multi-valued and derived attributes are also discussed.
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The document discusses database normalization. It provides examples of entity-relationship diagrams for various scenarios like a hospital, police case tracking, and employee-project relationships. It explains how to identify entities, attributes, and relationships. Primary keys are assigned and many-to-many relationships are resolved using linking tables. The goals of normalization are outlined as removing repeating groups of data and attributes not fully dependent on primary keys to satisfy first, second, and third normal form.
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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.
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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.
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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.
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This document provides an Excel list of courses for the summer 2018 term including subject, course number, title, campus, schedule type, dates, times, locations, and other details. It shows accounting and business administration courses at the undergraduate level. The data could be used to analyze course enrollment trends, popularity of classes by concentration, and ratios of online versus on-campus courses.
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This document outlines a 12-step process for normalizing a database to eliminate redundant data and improper relationships between tables. The steps include: 1) creating a narrative of the business needs, 2) identifying entities and attributes, 3) grouping attributes with entities, 4) adding primary keys, 5) evaluating entities and attributes, 6) determining relationships between entities, 7) resolving many-to-many relationships, 8) implementing the entity-relationship diagram by creating tables and fields, 9) adding surrogate primary keys, 10) defining foreign key relationships, and 11) creating unique indexes on tables. The document provides an example of normalizing an employee database using this 12-step method.
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INF 103(ASH) Learn/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
INF 103 Week 1 DQ 1 How Do You Currently Use Information Technology
INF 103 Week 1 DQ 2 Innovations in Hardware and Software
INF 103 Week 2 DQ 1 Copyright and Fair Use
INF 103 Week 2 DQ 2 Searching for Information
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Entity-Relationship Data Model in DBMS
It is a semantic data model that is used for the graphical representation of the conceptual database design. The semantic data models provide more constructs that is why a database design in a semantic data model can contain/represent more details. With a semantic data model, it becomes easier to design the database, at the first place, and secondly it is easier to understand later. We also know that conceptual database is our first comprehensive design. It is independent of any particular implementation of the database, that is, the conceptual database design expressed in E-R data model can be implemented using any DBMS. For that we will have to transform the conceptual database design from E-R data model to the data model of the particular DBMS. There is no DBMS based on the E-R data model, so we have to transform the conceptual database design anyway.
03 Ch3 Notes Revised
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.
Data Entry in CSpro
This document provides information about using CSPro software for data entry of a household health and population survey. It discusses installing and setting up the software, organizing files and folders, navigating forms during data entry, performing data checks and modifications, and saving data files. The key points are: CSPro allows for data entry, verification, consistency checks, and tabulation. Forms are linked to dictionaries to define the order of data entry. Error messages will appear if entered data is invalid or inconsistent. Incomplete questionnaires can be saved partially and completed later.
Data modelingpresentation
This document introduces data modeling and its key concepts. It defines a data model as a precise description of data in a system, including conceptual, logical, and physical models. Data models are created to develop sound database designs that avoid anomalies. Common data modeling approaches are introduced, including entity-relationship models and UML. The document outlines the process of creating an ER model, which involves identifying entities, attributes, primary keys, and relationships between entities. Relationship cardinality and different attribute types such as composite, multi-valued and derived attributes are also discussed.
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Data modeling is the process of exploring what data needs to be stored and then creating data structures accordingly. It involves listing relevant entities, their attributes, and relationships between entities. Tables are then created to represent the entities and relationships with keys used to represent the relationships.
Skipl List implementation - Part 2
This document describes an assignment to implement skip lists to solve queries on student data. Students are represented by unique IDs indicating their degree type, year, department, and enrollment number. Skip lists will be built on a master list of all students and subject-specific lists. Queries include finding B.Tech 4th year students in departments 2-3, students enrolled in Algorithms but not IR from departments 3-5, counts of students in both/either Algorithms or IR, and generating DOT files tracing the query solutions. The program must be submitted with documentation and output comparisons for each query trace in red.
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This document provides information on employee review and development processes. It discusses establishing a culture of self-evaluation and accountability to encourage professional growth. The review process involves reflection on one's actions and consequences to improve effectiveness. It should be a meaningful dialogue between an employee and their manager based on evidence to validate self-evaluations and identify next steps. The goal is to improve teaching and learning experiences for children.
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The document discusses database normalization. It provides examples of entity-relationship diagrams for various scenarios like a hospital, police case tracking, and employee-project relationships. It explains how to identify entities, attributes, and relationships. Primary keys are assigned and many-to-many relationships are resolved using linking tables. The goals of normalization are outlined as removing repeating groups of data and attributes not fully dependent on primary keys to satisfy first, second, and third normal form.
Normalization
The document describes the process of normalizing a database about projects and employees. It starts with a single table that has repeating groups and other issues. Through removing redundant data and splitting into multiple tables based on dependencies, it normalizes the data into third normal form with three tables: a projects table, employees table, and rates table. This reduces data redundancy and inconsistency while making the database structure more efficient and queries easier.
Er diagram practical examples
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.
Entity Relationship Diagram
Entity Relationship Diagrams (ERDs) are used to model relationships between entities in a database. The document discusses ERD components like entities, relationships, cardinality, and attributes. It provides an example of an ERD for a company with departments, supervisors, employees, and projects. Key entities are identified and their relationships and attributes are represented in the example ERD diagrams.
Erd examples
The document describes an entity relationship (ER) diagram for a university database. The ER diagram models entities such as students, departments, courses, and sections. It also models attributes of these entities and relationships between entities such as students having a major department, courses being offered by departments, and students receiving grades in sections.
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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.
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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.
Entity relationship diagram (erd)
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.
Erd practice exercises
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.
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Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
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1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
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For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
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Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
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Aen007 Kenigsberg 091807
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CIS 515 Enhance teaching / snaptutorial.com
For more classes visit
www.snaptutorial.com
This Tutorial contains 2 Set of Papers for each Assignment
CIS 515 Week 1 Assignment 1 Accessing Oracle (2 Papers)
Db lec 02_new
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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.
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MISY 3331 Advanced Database ConceptsAssignment 3Dr. Sotirios .docx
MISY 3331 Advanced Database Concepts
Assignment 3
Dr. Sotirios Zygiaris
[email protected]
Room:F084, tel. ext 5471
10% of your final grade (Covers chapters 7,8,10,11)
1. In Exercise 2.6, related to sales forecasting, the following business requirements were set. A-Oil & Chemical is chemical company that plans to create a database to forecast sales.
· A salesperson is responsible for a lead to sales. Each lead consists of the responsible salesperson, the customer targeted, date occur, projected date, projected sale amount and possibility of the sale to occur.
· Each Salesperson is specified by : First name, last name, telephone, date of hire
· Each customer is specified by title, address, telephone
· Leads that became sales are marked as “s” for success. Leads that fail are marked as “F” for fail. Leads that not have a final outcome yet are marked as “I” for idle.
The following diagram reflects the design for the database.
Guidelines
1. Attend and participate in assignment labs
2, . For each of the questions above create a clear screen shot that will include the database name, the SQL command and the produced results. Make sure that you have tested the results for correctness
3. show your work to your professor and get green light allowing you to submit assignment 1. Instructor will sign the evaluation rubric allowing you to submit.
7. Submit report on line on BB and print the report and hand it to your instructor. For late submissions 2 marks off for late submission penalty applies.
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1. Create a view VE1 that will customer_id, cust_title and the total amount for each customer.
2. You want the same group results as in 1 but only for customers with total amount more than 25,000 (HAVING). Can you do it with a consecutive view VE2 from VE1, if not why? If cannot do it as consecutive do it as new view VE3.
3. Create a view VV4 to list customer_id, amount, possibility, cust_title. Create a consecutive view from VV4, named High_Possibility, for leads with possibility >80. Create a consecutive view from VV4 called TX_CUST_LIST to list the same three attributes for only the Texas customers. Why you cannot do It?
4. Using the ROUND function create an SQL query that will COUNT LEADS in possibilities in 10S. Show only 10s with that counted more than three possibilities. Shorted by 10s.
5. Using the FLOOR function show create an SQL query that will COUNT LEADS in AMOUNTS IN EVERY 5000 but with only for leads with amount more than the average amount. Shorted by 5000s.
6. Write an SQL query that will display the customers as customer title in capital, underscore, City with first letter in Capital and the rest in lower characters, dot, state in capital,dot, and zip code inside brackets [], dot , telephone the first three character in parenthesis followed by a dash . Example:
NCR_Houston.TX.[55120].(345)-99345625
7. Write an SQL query that will lead list all leads with expected day 2000 days before today.
8. Us ...
Bc0041
A foreign key is a column that references the primary key of another table. It must match a value in the referenced primary key. Unlike primary keys, foreign keys can contain null values. Primary keys uniquely identify rows in a table, while foreign keys reference primary keys to link data between tables.
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Then, we present a case of how we've combined those techniques to build Smart Canvas (www.smartcanvas.com), a service that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
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- 3. tables queries (select some data only) macros forms (on screen) reports (on paper) what the user can see add a new student see all students see all in Year 13 list of all students list of all in Year 13 ? Design a database to store personal information of 6 th Form students
- 4. Primary key? Starter £4.00 3 13 1125 £2.00 2 12 1125 £4.00 1 13 1124 £3.00 1 14 1123 £2.00 2 12 1123 £4.00 3 13 1122 £2.00 2 12 1122 price quantitySold stockNum orderNum
- 6. Project number Project name Employee number Employee name Rate category Hourly rate 1. consider single table Consider the Employees and Projects table on the webpage 2. assign primary key(s)? Project number Project name Employee number Employee name Rate category Hourly rate 5. spot another (less obvious) Employee number Employee name Rate category Hourly rate Project number Employee number 6. …and remove it Employee number Employee name Rate category Project number Employee number Project number Project name Rate category Hourly rate 3. spot repeated groups of data… Project number Project name Employee number Employee name Rate category Hourly rate 4. …and remove them Employee number Employee name Rate category Hourly rate Project number Employee number Project number Project name
- 10. form student ONE form(s) contains ONE student(s) MANY MANY event world record ONE event(s) has ONE world record(s) MANY MANY employee project ONE employee(s) can work on ONE project(s) MANY MANY ( ) ( ) ( ) ( ) ( ) ( )
- 11. event world record ONE event(s) has ONE world record(s) MANY MANY ( ) ( ) event world record might as well merge into the one table (nothing to be gained from having two) a ONE-to-ONE relationship
- 12. form student ONE form(s) contains ONE student(s) MANY MANY ( ) ( ) This is what you are after It makes your database more efficient a ONE-to-MANY relationship form student
- 13. employee project ONE employee(s) can work on ONE project(s) MANY MANY ( ) ( ) Impossible to do in your database! Needs to be resolved… a MANY-to-MANY relationship employee project employee project employee_project Use a ‘link’ table between them
- 17. publisher book author film actor director student course customer product stock item supplier staff employee project Others to practice with
- 18. Project number Project name Employee number Employee name Rate category Hourly rate 1. consider single table Consider the Employees and Projects table on the webpage 2. assign primary key(s)? Project number Project name Employee number Employee name Rate category Hourly rate 5. spot another (less obvious) Employee number Employee name Rate category Hourly rate Project number Employee number 6. …and remove it Employee number Employee name Rate category Project number Employee number Project number Project name Rate category Hourly rate 3. spot repeated groups of data… Project number Project name Employee number Employee name Rate category Hourly rate 4. …and remove them Employee number Employee name Rate category Hourly rate Project number Employee number Project number Project name
- 19. Impossible to do in your database! Needs to be ‘resolved’… a MANY-to-MANY relationship employee project 1. first ERD Consider the Employees and Projects table again: employee project employee_project Use a ‘link’ table between them 2. second ERD employee project employee_project Employee number Employee name Rate category Hourly rate Employee number Project number Project number Project name 3. consider attributes 4. spot repeated group of data and remove employee project employee_project Employee number Employee name Rate category Employee number Project number Project number Project name rate Rate category Hourly rate