This document discusses different data models used to describe database structures, including the relational, entity-relationship, object-based, and semi-structured models. It focuses on explaining the entity-relationship model and its key concepts such as entities, attributes, relationships, cardinalities that define the number of relationships between entities, and participation constraints on entity involvement in relationships.
IDA - Fra forretningside til bundlinie: Eclipse følger dig hele vejenTonny Madsen
”Har du tænkt på at skifte til et leverandøruafhængigt udviklingsmiljø? Det er gratis, og du får ét udviklingsmiljø, som du kan programmere alt fra Java, C, C++ og PHP til databaser og webserver i. Vi får dig til at se værdien af værktøjet, og se flere forskellige eksempler på brugen af Eclipse i praksis.
Kom og hør formanden for eclipse.dk, Tonny Madsen, Direktør, RCP Kompaniet fortælle om Eclipse.
Eclipse er component-baseret, og du får indsigt i hvordan du sammensætter Eclipse til netop dine behov.”
*RDBMS ( Relational Database Management System)
*Network model
*Hierarchical Data Model
*Object-Oriented Model
*Attribute Types
*Relation Instance
*Relations are Unordered
*Database
*E-R Diagram for the Banking Enterprise
*Determining Keys from E-R Sets
IDA - Fra forretningside til bundlinie: Eclipse følger dig hele vejenTonny Madsen
”Har du tænkt på at skifte til et leverandøruafhængigt udviklingsmiljø? Det er gratis, og du får ét udviklingsmiljø, som du kan programmere alt fra Java, C, C++ og PHP til databaser og webserver i. Vi får dig til at se værdien af værktøjet, og se flere forskellige eksempler på brugen af Eclipse i praksis.
Kom og hør formanden for eclipse.dk, Tonny Madsen, Direktør, RCP Kompaniet fortælle om Eclipse.
Eclipse er component-baseret, og du får indsigt i hvordan du sammensætter Eclipse til netop dine behov.”
*RDBMS ( Relational Database Management System)
*Network model
*Hierarchical Data Model
*Object-Oriented Model
*Attribute Types
*Relation Instance
*Relations are Unordered
*Database
*E-R Diagram for the Banking Enterprise
*Determining Keys from E-R Sets
I am heartily thankful to our respected director sir for giving me such a great opportunity to giving this presentation.
I am also thankful to our respect Teachers for helping me in making of this presentation.
Common Data Model - A Business Database!Pedro Azevedo
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Common Data Service – A Business Database!Pedro Azevedo
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What is dimension modeling? ,
Difference between ER modeling and dimension modeling,
What is a Dimension? ,
What is a Fact?
Start Schema ,
Snow Flake Schema ,
Difference between Star and snow flake schema ,
Fact Table ,
Different types of facts
Dimensional Tables,
Fact less Fact Table ,
Confirmed Dimensions ,
Unconfirmed Dimensions ,
Junk Dimensions ,
Monster Dimensions ,
Degenerative Dimensions ,
What are slowly changing Dimensions? ,
Different types of SCD's,
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
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at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2. Subject Name Code Credit Hours
Database System COMP 219 3
Data Models
• A collection of tools for describing
– Data
– Data relationships
– Data semantics
– Data constraints
• Relational model
• Entity-Relationship data model (mainly for database design)
• Object-based data models (Object-oriented and Object-relational)
• Semistructured data model (XML)
• Other older models:
– Network model
– Hierarchical model
3. Subject Name Code Credit Hours
Database System COMP 219 3
Data Models
• A collection of Conceptual tools for describing
– Data
– Data relationships
– Data semantics
– Data constraints
• Different data Models
• - Relational model
- Entity-Relationship data model (Conceptual Model-mainly for database design)
- Object-based data models (Object-oriented and Object-relational)
- Semi structured data model (XML)
• Other older models:
– Network model
– Hierarchical model
A Data model is a set of concepts that can be used to
describe the structure of the db.
4. Subject Name Code Credit Hours
Database System COMP 219 3
Entity-Relationship data model
• It is a high level conceptual data model that describes the structure of db
in terms of entities, relationship among entities & constraints on them..
• Basic Concepts of E-R Model:
- Entity
- Entity Set
- Attributes
- Relationship
- Relationship set
- Identifying Relationship
I
5. Subject Name Code Credit Hours
Database System COMP 219 3
Entity-Relationship data model
• Entity:
-It is a an object that exists in the real world.
• Example:
- Person, Employee, Car, Home etc..
Object with conceptual Existence
- Account, loan, job etc…
6. Subject Name Code Credit Hours
Database System COMP 219 3
Entity-Relationship data model
• Entity Set:
- A set of entities of the same type.
• Attributes:
- A set of properties that describe an
entity.
7. Subject Name Code Credit Hours
Database System COMP 219 3
• Types of Attributes:
• Simple (or) atomic vs. Composite:
• - An attribute which cant be sub divided. (Eg.Age)
• - An attribute which can be divided into sub parts is called
• as composite attribute.
e.g.. Address- Apartment no.
- Street
- Place
- City
- District
Single Valued vs. Multivalued:
• -An attribute having only one value (e.g.. Age,eid,sno)
• - An attribute having multiple values (e.g.. Deptlocat- A dept can be located in
several places)
Entity-Relationship data model
8. Subject Name Code Credit Hours
Database System COMP 219 3
Entity-Relationship data model
• Stored Vs Derived
• - Stored attribute is one that has some value where as
derived attribute is a one where its value is derived from sa.
• -E.g.. SA-DOB
• DA- Age derived from DOB.
• Key Attribute:
• - An attribute which is used to uniquely identify records.
• E.g.. eid, sno, dno
9. Subject Name Code Credit Hours
Database System COMP 219 3
Entity-Relationship data model
• Relationship:
• - It is an association among several
entities. It specifies what type of relationship
exists between entities.
10. Subject Name Code Credit Hours
Database System COMP 219 3
• Relationship set:
• - It is a set of relationships of the same type.
Entity-Relationship data model
1 AA 1000
2 BB 2000
100 FFF 10000
1 AA AC
2 BB AD
100 FFF SD
Entity
E
N
TI
T
Y
S
E
T
DEPT. ENTITY TYPERelationship SetEmp.. ENTITY TYPE
11. Subject Name Code Credit Hours
Database System COMP 219 3
• Weak Entity Set:
• - No key attributes.
• Identifying Relationship:
• - The relationship associated with the weak
entity type
Entity-Relationship data model
12. Subject Name Code Credit Hours
Database System COMP 219 3
Constraints
• Two of the most important constraints are
• a. Mapping Constraints
• b. Participation constraints
»Participation constraints
Total Participation Partial Participation
13. Subject Name Code Credit Hours
Database System COMP 219 3
a. Mapping Cardinalities:
Mapping Cardinalities OR CARDINALITY RATIOS, EXPRESSS THE NUMBER OF
ENTITIES TO WHICH ANOTHER ENTITY CAN BE ASSOCIATED VIA A
RELATIONSHIPSET.
• Several types of Mapping Cardinalities. They are,
• a.i. One-to-One
• An entity in set A is associated with at most one entity in set B and
vice versa.
e1
e2
e3
d1
d2
d3
Employee Dept.Works for
14. Subject Name Code Credit Hours
Database System COMP 219 3
• a.i. One-to-many
• An entity in set A is associated with zero or more
no. of entities in set B and an entity in B is associated with
at most one entity in A.
a. Mapping Cardinalities:
e1
e2
e3
d1
d2
d3
Employee Dept.Works for
15. Subject Name Code Credit Hours
Database System COMP 219 3
a. Mapping Cardinalities:
a.i. Many-to-One
One or more no. of entities in set A is associated with at
most one entity in B. An entity in B can be associated with any no.
of entities in A.
e1
e2
e3
e4
d1
d2
d3
Employee Dept.Works for
16. Subject Name Code Credit Hours
Database System COMP 219 3
• a.i. Many-to-Many
• One or more no. of entities in set A
is associated with one or more no. of
entities in set B.
a. Mapping Cardinalities:
e1
e2
e3
e4
d1
d2
d3
Employee Dept.Works for
17. Subject Name Code Credit Hours
Database System COMP 219 3
• The participation of an entity set E in a relationship set R is
said to be total if every entity in E participates in atleast one
relationship in R.
b. Participation Constraints:
Total Participation
Partial Participation:
The participation of an entity set E in a relationship set R is said to be
partial if only a few of the entities in E participated in relationship in R.
18. Subject Name Code Credit Hours
Database System COMP 219 3
b. Participation Constraints:
• E.G..
Partial participation Total participation
Employee
Dept.
Manages