Data modeling is the process of creating a visual representation of data within an information system to illustrate the relationships between different data types and structures. The goal is to model data at conceptual, logical, and physical levels to support business needs and requirements. Conceptual models provide an overview of key entities and relationships, logical models add greater detail, and physical models specify how data will be stored in databases. Data modeling benefits include reduced errors, improved communication and performance, and easier management of data mapping.
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
Discover the fundamentals of structuring data effectively with "Introduction-to-Data-Modeling." This guide delves into the principles of Data Modeling & Normalization, offering a straightforward approach to organizing data for efficient analysis and retrieval. Explore essential concepts and techniques to optimize data structures, enabling smoother operations and clearer insights.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
Discover the fundamentals of structuring data effectively with "Introduction-to-Data-Modeling." This guide delves into the principles of Data Modeling & Normalization, offering a straightforward approach to organizing data for efficient analysis and retrieval. Explore essential concepts and techniques to optimize data structures, enabling smoother operations and clearer insights.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
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 .
Informatica Data Modelling : Importance of Conceptual ModelsZaranTech LLC
50-55 hours Training + Assignments + Actual Project Based Case Studies
All attendees will receive,
Assignment after each module, Video recording of every session
Notes and study material for examples covered.
Access to the Training Blog & Repository of Materials
Itlc hanoi ba day 3 - thai son - data modellingVu Hung Nguyen
https://www.facebook.com/events/535707009911719/
(ITLC HN) BA DAY3: CHIẾN LƯỢC THIẾT KẾ MÔ HÌNH DỮ LIỆU
1.Thời gian: 18:30 - 21:00, 10/9/2015 (Tối thứ 5)
2. Địa điểm: HATCH - Tầng 14 - 195B Đội Cấn (http://nest.hatch.vn/nest-14.html)
3. Tổ chức: Ban tổ chức sự kiện ITLC Hà Nội
4. Chương trình:
18:30 - 18:45: Đón khách
18:45 - 19:00: Nguyễn Mạnh Cường (Fis) Giới thiệu ITLC Hà Nội
19:00 - 19:30: Thái Sơn chia sẻ “Một số mô hình dữ liệu mẫu trong phân tích nghiệp vụ”
19:30 - 19:50: Lê Phú Cường chia sẻ “Chiến lược lưu giữ dữ liệu lịch sử”
19:50 - 20:50: Panel cùng với: Thái Sơn, Lê Phú Cường, Lê Văn Duy
20:50 - 21:00: Tổng kết sự kiện và chụp hình kỷ niệm
5. Đăng ký: theo form sau đây http://topi.ca/baday3
6. Phí tham gia: 100K
7. Liên hệ, giải đáp: Lê Đại Nam: 0902-261-239
Xem thêm sự kiện BA1 tại đây: https://www.facebook.com/events/1616821285258614/
Xem thêm sự kiện BA2 tại đây: https://www.facebook.com/events/1669594633274443/
This reading introduces you to data modeling and different types of data models. Data models help keep data consistent and enable people to map out how data is organized. A basic understanding makes it easier for analysts and other stakeholders to make sense of their data and use it in the right ways.
Important note: As a junior data analyst, you won't be asked to design a data model. But you might come across existing data models your organization already has in place.
3. Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.
3. Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.
FORMALIZATION & DATA ABSTRACTION DURING USE CASE MODELING IN OBJECT ORIENTED ...cscpconf
In object oriented analysis and design, use cases represent the things of value that the system performs for its actors in UML and unified process. Use cases are not functions or features.
They allow us to get behavioral abstraction of the system to be. The purpose of the behavioral abstraction is to get to the heart of what a system must do, we must first focus on who (or what)
will use it, or be used by it. After we do this, we look at what the system must do for those users in order to do something useful. That is what exactly we expect from the use cases as the
behavioral abstraction. Apart from this fact use cases are the poor candidates for the data abstraction. Rather the do not have data abstraction. The main reason is it shows or describes
the sequence of events or actions performed by the actor or use case, it does not take data in to account. As we know in earlier stages of the development we believe in ‘what’ rather than
‘how’. ‘What’ does not need to include data whereas ‘how’ depicts the data. As use case moves around ‘what’ only we are not able to extract the data. So in order to incorporate data in use cases one must feel the need of data at the initial stages of the development. We have developed the technique to integrate data in to the uses cases. This paper is regarding our investigations to take care of data during early stages of the software development. The collected abstraction of data helps in the analysis and then assist in forming the attributes of the candidate classes. This makes sure that we will not miss any attribute that is required in the abstracted behavior using use cases. Formalization adds to the accuracy of the data abstraction. We have investigated object constraint language to perform better data abstraction during analysis & design in unified paradigm. In this paper we have presented our research regarding early stage data abstraction and its formalization.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
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 .
Informatica Data Modelling : Importance of Conceptual ModelsZaranTech LLC
50-55 hours Training + Assignments + Actual Project Based Case Studies
All attendees will receive,
Assignment after each module, Video recording of every session
Notes and study material for examples covered.
Access to the Training Blog & Repository of Materials
Itlc hanoi ba day 3 - thai son - data modellingVu Hung Nguyen
https://www.facebook.com/events/535707009911719/
(ITLC HN) BA DAY3: CHIẾN LƯỢC THIẾT KẾ MÔ HÌNH DỮ LIỆU
1.Thời gian: 18:30 - 21:00, 10/9/2015 (Tối thứ 5)
2. Địa điểm: HATCH - Tầng 14 - 195B Đội Cấn (http://nest.hatch.vn/nest-14.html)
3. Tổ chức: Ban tổ chức sự kiện ITLC Hà Nội
4. Chương trình:
18:30 - 18:45: Đón khách
18:45 - 19:00: Nguyễn Mạnh Cường (Fis) Giới thiệu ITLC Hà Nội
19:00 - 19:30: Thái Sơn chia sẻ “Một số mô hình dữ liệu mẫu trong phân tích nghiệp vụ”
19:30 - 19:50: Lê Phú Cường chia sẻ “Chiến lược lưu giữ dữ liệu lịch sử”
19:50 - 20:50: Panel cùng với: Thái Sơn, Lê Phú Cường, Lê Văn Duy
20:50 - 21:00: Tổng kết sự kiện và chụp hình kỷ niệm
5. Đăng ký: theo form sau đây http://topi.ca/baday3
6. Phí tham gia: 100K
7. Liên hệ, giải đáp: Lê Đại Nam: 0902-261-239
Xem thêm sự kiện BA1 tại đây: https://www.facebook.com/events/1616821285258614/
Xem thêm sự kiện BA2 tại đây: https://www.facebook.com/events/1669594633274443/
This reading introduces you to data modeling and different types of data models. Data models help keep data consistent and enable people to map out how data is organized. A basic understanding makes it easier for analysts and other stakeholders to make sense of their data and use it in the right ways.
Important note: As a junior data analyst, you won't be asked to design a data model. But you might come across existing data models your organization already has in place.
3. Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.
3. Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.
FORMALIZATION & DATA ABSTRACTION DURING USE CASE MODELING IN OBJECT ORIENTED ...cscpconf
In object oriented analysis and design, use cases represent the things of value that the system performs for its actors in UML and unified process. Use cases are not functions or features.
They allow us to get behavioral abstraction of the system to be. The purpose of the behavioral abstraction is to get to the heart of what a system must do, we must first focus on who (or what)
will use it, or be used by it. After we do this, we look at what the system must do for those users in order to do something useful. That is what exactly we expect from the use cases as the
behavioral abstraction. Apart from this fact use cases are the poor candidates for the data abstraction. Rather the do not have data abstraction. The main reason is it shows or describes
the sequence of events or actions performed by the actor or use case, it does not take data in to account. As we know in earlier stages of the development we believe in ‘what’ rather than
‘how’. ‘What’ does not need to include data whereas ‘how’ depicts the data. As use case moves around ‘what’ only we are not able to extract the data. So in order to incorporate data in use cases one must feel the need of data at the initial stages of the development. We have developed the technique to integrate data in to the uses cases. This paper is regarding our investigations to take care of data during early stages of the software development. The collected abstraction of data helps in the analysis and then assist in forming the attributes of the candidate classes. This makes sure that we will not miss any attribute that is required in the abstracted behavior using use cases. Formalization adds to the accuracy of the data abstraction. We have investigated object constraint language to perform better data abstraction during analysis & design in unified paradigm. In this paper we have presented our research regarding early stage data abstraction and its formalization.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
ER(Entity Relationship) Diagram for online shopping - TAE
Data Modeling.docx
1. Data Modeling
Data modeling is the process of creating a visual representation of either a whole
information system or parts of it to communicate connections between data points and
structures.
Goal Of Data Model
The goal is to illustrate the types of data used and stored within the system, the
relationships among these data types, the ways the data can be grouped and organized
and its formats and attributes.
Data models are built around business needs.Rules and requirements are defined
upfront through feedback from business stakeholders so they can be incorporated into
the design of a new system or adapted in the iteration of an existing one.
Data can be modeled at various levels of abstraction.
The process begins by collecting information about business requirements from
stakeholders and end users.
These business rules are then translated into data structures to formulate a concrete
database design.
A data model can be compared to a roadmap, an architect’s blueprint or any formal
diagram that facilitates a deeper understanding of what is being designed.
Data modeling employs standardized schemas and formal techniques. This provides a
common, consistent, and predictable way of defining and managing data resources
across an organization, or even beyond.
Ideally, data models are living documents that evolve along with changing business
needs. They play an important role in supporting business processes and planning IT
architecture and strategy. Data models can be shared with vendors, partners, and/or
industry peers.
Types of data models
Like any design process, database and information system design begins at a high
level of abstraction and becomes increasingly more concrete and specific.
Data models can generally be divided into three categories, which vary according to
their degree of abstraction.
The process will start with a conceptual model, progress to a logical model and
conclude with a physical model.
2. Conceptual data models.
They are also referred to as domain models and offer a big-picture view of what the
system will contain, how it will be organized, and which business rules are involved.
Conceptual models are usually created as part of the process of gathering initial
project requirements.
Typically, they include:
a) entity classes (defining the types of things that are important for the business to
represent in the data model),
b) their characteristics and constraints,
c) the relationships between them,
d) relevant security and data integrity requirements.
3. Logical data models.
They are less abstract and provide greater detail about the concepts and relationships
in the domain under consideration.
One of several formal data modeling notation systems is followed. These indicate data
attributes, such as data types and their corresponding lengths, and show the
relationships among entities.
Logical data models don’t specify any technical system requirements.
4. Physical data models.
They provide a schema for how the data will be physically stored within a database.
They offer a finalized design that can be implemented as a relational database,
including associative tables that illustrate the relationships among entities as well as
the primary keys and foreign keys that will be used to maintain those relationships.
Benefits of data modeling
Data modeling makes it easier for developers, data architects, business analysts, and
other stakeholders to view and understand relationships among the data in a database
or data warehouse. In addition, it can:
Reduce errors in software and database development.
Increase consistency in documentation and system design across the enterprise.
Improve application and database performance.
Ease data mapping throughout the organization.
Improve communication between developers and business intelligence teams.
Ease and speed the process of database design at the conceptual, logical and
physical levels.
IMPORTANCE OF DATA MODELS
Modelling can guide your exploration:
It can help you figure out what questions to ask
It can help to reveal key design decisions
5. It can help you to uncover problems
Modelling can help us check our understanding
Reason about the model to understand its consequences
Does it have the properties we expect?
Animate the model to help us visualize/validate software behavior
Modelling can help us communicate
Provides useful abstractions that focus on the point you want to make
without overwhelming people with detail
DATA MODELING – THE PROCESS
Data modeling is a method used to define and analyze data requirements
needed to support the business processes of an organization; it defines data
elements, their structures and relationships between them.
It is the process of creating a data model for the data to be stored in a
Database which helps in the visual representation of data and enforces
business rules, regulatory compliances, and government policies on the data.
Data Modelling Process.
Step 1
Initially, the data requirements are recorded as a conceptual data model
which is essentially a set of technology independent specifications about the
data and is used to discuss initial requirements with the business stakeholders.
Step 2
The conceptual model is then translated into a logical data model, which
documents structures of the data that can be implemented in databases.
Implementation of one conceptual data model may require multiple logical
data models.
Step 3
The last step in data modeling is transforming the logical data model to a
physical data model that organizes the data into tables, and accounts for
access, performance and storage details.
DATA MODEL METHODOLOGY
The two major methodologies used to create a data model are
The Entity‐ Relationship (ER) approach.
The Object‐ oriented Model.
1. Entity‐ relationship Model
Entity – Relationship model (ER model for short) is the conceptual design of a
database that includes its entities and relationships.
In other words, it is an abstract way to describe a database. It usually starts with a
relational database, which stores data in tables.
Some of the data in these tables point to data in other tables.
6. EXAMPLE
An Aerodrome entry in the database could be related to several Runways that belong
to this Aerodrome.
The ER model would say that the “Aerodrome” is an entity, and each “Runway” is an
entity
These entities might have a list of properties called “Attributes” like the Identifier,
Name, Length, Width, etc. and the relationships between the Aerodrome and the
Runway is in one direction “having Runways” and from the other direction “situated
at one Aerodrome”.
Diagrams created to design these entities and relationships are called entity–
relationship diagrams or ER diagrams.
Figure for Entity-Relationship Diagram
2. Object‐ oriented Model
Object‐ oriented modeling (OOM), also called object‐ oriented programming (OOP)
is a modeling paradigm mainly used in computer programming.
The object‐ oriented paradigm assists the programmer to address the complexity of a
problem domain by considering the problem not as a set of functions that can be
performed but primarily as a set of related, interacting Objects.
The modeling task then is specifying, for a specific context, those Objects (or the
Class the Objects belongs to), their respective set of Properties and Methods, shared
by all Objects members of the Class.
The Model description or Schema may grow in complexity to require a Notation.
Many notations have been proposed, based on different paradigms, diverged, and
converged in a more popular one known as UML (Unified Modeling Language).
Let’s take the same example as in the entity‐ relationship model about the Aerodrome
and Runway and consider them a Class of object as defined in the UML, where they
will also be defined by a list of properties and constrained by a data type, besides the
two classes they also have a relationship as shown in next figure where a Class
diagram is shown.
7. Figure for Class Diagram
Both object‐ oriented modeling and Entity‐ relation model are quite common in
software today. Since relational databases don't store objects directly, there is a
general need to bridge the two worlds by using different approaches to cope with this
problem like Object‐ relational mapping or Object Database methodology, subjects
which are out of the scope of this material.