Student POST Database processing models showcase the logical s.docxorlandov3
Student POST:
Database processing models showcase the logical structure of a database. The most commonly used model is the Relational database model that sorts the data in a table that consist of rows and columns. The column holds the attributes of the entity and rows hold the data of a particular instance of the entities. The major advantage of the Relational model is that it is in the table form and hence easier for users to understand, manage and work with the data. And, with the primary key and foreign key concepts, the data can be uniquely identified, stored in different entities and retrieved effectively with the relationships. The other advantage is that with the relational model, SQL language can be used to work with the data which is simple to understand and most widely used. The disadvantage of relational model could be the financial cost that is higher in comparison as the specific software needs to be in place and the regular maintenance needs to be performed that requires highly skilled manpower. And, the complexity of the database can be further increased when the volume of the data keep in increasing. Also, there is the limitation in the length of fields stored as different data types in relational model (Joseph & Paul, 2009).
The other processing model is the Object-oriented model that depicts database as the collection of objects. The advantage of this model is that it is compatible to work with complex data sets with the use of Object IDs and object-oriented programming. It’s disadvantage is that object databases are not commonly used and the complexity can hamper the performance of database. The other type of database model is the Entity-Relationship model which is mostly used for the conceptual design of database. It pictures the entities, several attributes that falls within the domain of that entity and the cardinality of relationship between them. It’s advantage is that the E-R diagram is easily understandable by the users at the first glance and thus can effectively work with the data in no time and can point out the discrepancies in the data. The other advantage is that it can be easily converted to other models if required by the business. The disadvantage of Entity-Relationship is that the industry standard notations for the diagram is not defined and thus can create confusion to the users. This model is only suitable for high-level database design (S.J.D.,2020).
2Nd Student POST :
Database models or commonly referred to as schemas help represent the structure of a database and its format which is run by a DBMS. Database model uses vary depending on user specifications.
Types of database models
1.
Network model
This network model uses a structure similar to that of a hierarchical model. The model permits multiple parents, which is a tree-like structure model. This model emphasizes two basic concepts; records and sets. Records hold file hierarchy and sets define the many-to-many relationship .
Student POST Database processing models showcase the logical s.docxorlandov3
Student POST:
Database processing models showcase the logical structure of a database. The most commonly used model is the Relational database model that sorts the data in a table that consist of rows and columns. The column holds the attributes of the entity and rows hold the data of a particular instance of the entities. The major advantage of the Relational model is that it is in the table form and hence easier for users to understand, manage and work with the data. And, with the primary key and foreign key concepts, the data can be uniquely identified, stored in different entities and retrieved effectively with the relationships. The other advantage is that with the relational model, SQL language can be used to work with the data which is simple to understand and most widely used. The disadvantage of relational model could be the financial cost that is higher in comparison as the specific software needs to be in place and the regular maintenance needs to be performed that requires highly skilled manpower. And, the complexity of the database can be further increased when the volume of the data keep in increasing. Also, there is the limitation in the length of fields stored as different data types in relational model (Joseph & Paul, 2009).
The other processing model is the Object-oriented model that depicts database as the collection of objects. The advantage of this model is that it is compatible to work with complex data sets with the use of Object IDs and object-oriented programming. It’s disadvantage is that object databases are not commonly used and the complexity can hamper the performance of database. The other type of database model is the Entity-Relationship model which is mostly used for the conceptual design of database. It pictures the entities, several attributes that falls within the domain of that entity and the cardinality of relationship between them. It’s advantage is that the E-R diagram is easily understandable by the users at the first glance and thus can effectively work with the data in no time and can point out the discrepancies in the data. The other advantage is that it can be easily converted to other models if required by the business. The disadvantage of Entity-Relationship is that the industry standard notations for the diagram is not defined and thus can create confusion to the users. This model is only suitable for high-level database design (S.J.D.,2020).
2Nd Student POST :
Database models or commonly referred to as schemas help represent the structure of a database and its format which is run by a DBMS. Database model uses vary depending on user specifications.
Types of database models
1.
Network model
This network model uses a structure similar to that of a hierarchical model. The model permits multiple parents, which is a tree-like structure model. This model emphasizes two basic concepts; records and sets. Records hold file hierarchy and sets define the many-to-many relationship .
Introduction to Data and Information, database, types of database models, Introduction to DBMS, Difference between file management systems and DBMS, advantages & disadvantages of DBMS, Data warehousing, Data mining, Applications of DBMS, Introduction to MS Access, Create Database, Create Table, Adding Data, Forms in MS Access, Reports in MS Access.
A database model refers to the structure of a database and determines how the data within the database can be organized and manipulated. Let’s explore some common types of database models:
Relational Model: The most popular example, the relational model, organizes data into tables (also known as relations). Each table contains rows representing records and columns representing attributes. Relationships between tables are established using keys.
Hierarchical Model: Developed by IBM for IMS (Information Management System), this model arranges data in a tree-like structure. Each record is a tree node, and relationships follow a one-to-many pattern. It’s predictable and efficient for data access.
Network Model: This model allows many-to-many relationships between records. It’s more flexible than the hierarchical model but less common.
Entity–Relationship Model (ER Model): It represents entities, their attributes, and the relationships between them. ER diagrams visually depict these components.
Object Model: Used in object-oriented databases, it treats data as objects with properties and methods. It’s suitable for complex data structures.
Document Model: Commonly used in NoSQL databases, it stores data as documents (e.g., JSON or XML). Each document can have varying attributes.
Entity–Attribute–Value (EAV) Model: A flexible model where data is stored in a sparse matrix. It’s useful for handling dynamic attributes.
Star Schema: Primarily used in data warehousing, it simplifies complex data structures into a central fact table connected to dimension tables.
Introduction to Data and Information, Database, Types of Database models, Introduction to DBMS,
Difference between file management systems and DBMS, Advantages and Disadvantages of DBMS, Data
warehousing, Data mining, Application of DBMS, Introduction to MS Access, Create Database, Create
Table, Adding Data, Forms in MS Access, Reports in MS Access.
The importance of data models, Basic building blocks, Business rules, The evolution of data models, Degrees of data abstraction
Database design and Introduction to UML
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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.
Introduction to Data and Information, database, types of database models, Introduction to DBMS, Difference between file management systems and DBMS, advantages & disadvantages of DBMS, Data warehousing, Data mining, Applications of DBMS, Introduction to MS Access, Create Database, Create Table, Adding Data, Forms in MS Access, Reports in MS Access.
A database model refers to the structure of a database and determines how the data within the database can be organized and manipulated. Let’s explore some common types of database models:
Relational Model: The most popular example, the relational model, organizes data into tables (also known as relations). Each table contains rows representing records and columns representing attributes. Relationships between tables are established using keys.
Hierarchical Model: Developed by IBM for IMS (Information Management System), this model arranges data in a tree-like structure. Each record is a tree node, and relationships follow a one-to-many pattern. It’s predictable and efficient for data access.
Network Model: This model allows many-to-many relationships between records. It’s more flexible than the hierarchical model but less common.
Entity–Relationship Model (ER Model): It represents entities, their attributes, and the relationships between them. ER diagrams visually depict these components.
Object Model: Used in object-oriented databases, it treats data as objects with properties and methods. It’s suitable for complex data structures.
Document Model: Commonly used in NoSQL databases, it stores data as documents (e.g., JSON or XML). Each document can have varying attributes.
Entity–Attribute–Value (EAV) Model: A flexible model where data is stored in a sparse matrix. It’s useful for handling dynamic attributes.
Star Schema: Primarily used in data warehousing, it simplifies complex data structures into a central fact table connected to dimension tables.
Introduction to Data and Information, Database, Types of Database models, Introduction to DBMS,
Difference between file management systems and DBMS, Advantages and Disadvantages of DBMS, Data
warehousing, Data mining, Application of DBMS, Introduction to MS Access, Create Database, Create
Table, Adding Data, Forms in MS Access, Reports in MS Access.
The importance of data models, Basic building blocks, Business rules, The evolution of data models, Degrees of data abstraction
Database design and Introduction to UML
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In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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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.
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http://sandymillin.wordpress.com/iateflwebinar2024
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Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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Embracing GenAI - A Strategic ImperativePeter 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.
1. Dr. Pradeep Kumar Mallick
Associate Professor [II]
School of Computer Engineering,
Kalinga Institute of Industrial Technology (KIIT),
Deemed to be University,Odisha
Database Management Systems (CS 2004)
KALINGA INSTITUTE OF INDUSTRIAL
TECHNOLOGY
School Of Computer
Engineering
4 Credit Lecture Note 02
2. Data model is a collection of conceptual tools for describing data, data
relationships, data semantics and consistency constraints. That means a data
model provides a way to describe the design of a database.
It is relatively simple representation, usually graphical, of complex real-
world data structures.
Data modeling is considered as the most important part of the database
design process.
2
Data Model
3. Entity: An entity can be a real-world object, either animate or inanimate,
that can be easily identifiable. For example, in a school database, students,
teachers, classes, and courses offered can be considered as entities. All
these entities have some attributes or properties that give them their
identity.
Entity Set: An entity set is a collection of similar types of entities. An
entity set may contain entities with attribute sharing similar values. For
example, a Students set may contain all the students of a school; likewise a
Teachers set may contain all the teachers of a school from all faculties.
Attribute: Entities are represented by means of their properties,
called attributes. All attributes have values. For example, a student entity
may have name, class, and age as attributes.
Constraints: A constraint is a restriction placed on the data. Constraints are
important because they help to ensure data integrity
3
Data Model Basic Building Blocks
4. Relationship :The association among entities is called a relationship. For example,
an employee works_at a department, a student enrolls in a course. Here, Works_at
and Enrolls are called relationships.
4
4
Data Model Basic Building Blocks...
• One-to-One (1:1) Relationship:
• One-to-Many (1:M) Relationship:
• Many-to-Many (M:N) Relationship:
5. This database model organizes data into a tree-like-structure, with a single root,
to which all the other data is linked. The hierarchy starts from the Root data, and
expands like a tree, adding child nodes to the parent nodes.
In this model, a child node will only have a single parent node.
This model efficiently describes many real-world relationships like index of a
book, recipes etc.
In hierarchical model, data is organized into tree-like structure with one one-to-
many relationship between two different types of data, for example, one
department can have many courses, many professors and of-course many
students.
The hierarchical model was developed in the 1960s to manage large amount of
data for complex manufacturing projects
5
5
Hierarchical Model
6. Advantages:
Efficient storage for data that have a clear hierarchy
Parent/child relationship promotes conceptual simplicity & data
integrity
It is efficient with 1:M relationships
It promotes data sharing
Disadvantages:
It is complex to implement
It is difficult to manage
There are implementation limitations, that means it can’t represent
M:N relationships
There is no DDL and DML
There is lack of standards
6
Hierarchical Model…
7. This is an extension of the Hierarchical model.
In this model data is organized more like a graph, and are allowed to have more
than one parent node.
In this database model data is more related as more relationships are established
in this database model. Also, as the data is more related, hence accessing the
data is also easier and fast.
This database model was used to map many-to-many data relationships.
This was the most widely used database model, before Relational Model was
introduced
7
Network Model
8. Advantages:
It represents complex data relationships better than hierarchical models
It handles more relationship types, such as M: N and multi-parent
Data access is more flexible than hierarchical model
Improved database performance
It includes DDL and DML
Disadvantages:
System complexity limits efficiency
Navigational system yields complex implementation and management
Structural changes require changes in all application programs
Database contains a complex array of pointers that thread through a set
of records
Put heavy pressure on programmers due the complex structure
Networks can become chaotic unless planned carefully
8
Network Model….
9. In this model, data is organised in two-dimensional tables and the
relationship is maintained by storing a common field.
This model was introduced by E.F Codd in 1970, and since then it has been
the most widely used database model, infact, we can say the only database
model used around the world.
The basic structure of data in the relational model is tables. All the
information related to a particular type is stored in rows of that table.
Hence, tables are also known as relations in relational model.
Domain: It contains a set of atomic values that an attribute can take.
Attribute: It contains the name of a column in a particular table. Each
attribute Ai must have a domain, dom(Ai)
Relational instance: In the relational database system, the relational
instance is represented by a finite set of tuples. Relation instances do not
have duplicate tuples.
9
Relational Model
10. 10
Relational Model
Relational schema: A relational schema contains the name of the relation and name
of all columns or attributes.
Relational key: In the relational key, each row has one or more attributes. It can
identify the row in the relation uniquely
11. Advantages:
Changes in a table’s structure do not affect data access or application
programs
Tabular view substantially improves conceptual simplicity, thereby
promoting easier database design, implementation, management and
use
Have referential integrity controls ensure data consistency
RDBMS isolates the end-users from physical level details and
improves implementation and management simplicity
Disadvantages:
Conceptual simplicity gives relatively untrained people the tools to use
a good system poorly
It may promote islands of information problems as individuals and
departments can easily develop their own applications
11
Relational Model
12. ER model stands for an Entity-Relationship model. It is a high-level data
model. This model is used to define the data elements and relationship for a
specified system.
It develops a conceptual design for the database. It also develops a very
simple and easy to design view of data.
In ER modeling, the database structure is portrayed as a diagram called an
entity-relationship diagram.
Peter Chen first introduced the ER data model in 1976; it was the
graphical representation of entities and their relationships in a database
structure that quickly became popular.
12
Entity-Relationship(ER) Model
13. Advantages:
Visual modeling yields exceptional conceptual simplicity
Visual representation makes it an effective communication tool
It is integrated with dominant relational model
Disadvantages:
There is limited constraint representation
There is limited relationship representation
There is no DML
Loss of information content when attributes are removed from entities
to avoid crowded displays
13
Entity-Relationship(ER) Model
14. In object-oriented data model, both data and their relationships are
contained in a single structure called an object.
Like the relational model’s entity, an object is described by its factual
content. But quite unlike an entity, an object includes information about
relationships between the facts within the object, as well as information
about its relationships with other.
Attributes describe the properties of an object. Objects that share similar
characteristics are grouped in classes. Thus, a class is a collection of similar
objects with shared structure (attributes) and methods
14
Object-Oriented(OO) Model
15. Advantages:
Semantic content is added
Support for complex objects
Visual representation includes semantic content
Inheritance promotes data integrity
Disadvantages:
It is a complex navigational system
High system overheads slow transactions
Slow development of standards caused vendors to supply their own
enhancements, thus eliminating a widely accepted standard.
15
Object-Oriented(OO) Model
16. The object-oriented data model is somewhat spherical in nature, allowing
access to unique elements anywhere within a database structure, with
extremely high performance. But, it performs extremely poorly when
retrieving more than a single data item.
The relational data model is best suited for retrieval of groups of data, but
can also be used to access unique data items fairly efficiently
Thus, by combining the features of relational data model and object-
oriented data model, object-relational data model was created.
16
Object-Relational(OR) Model
17. The semi-structured data model permits the specification of data where
individual data items of the same type may have different sets of attributes.
The XML (Extensible Markup Language) is widely used to represent semi-
structured data. It supports unstructured data.
17
Semi-structured Model