The three level of data modeling
•Download as DOCX, PDF•
3 likes•1,949 views
The document compares conceptual, logical, and physical data models. Conceptual models show entities and relationships without attributes or keys. Logical models add attributes, primary keys, and foreign keys. Physical models specify tables, columns, data types, and foreign keys to represent the database implementation. The complexity increases from conceptual to logical to physical models.
Report
Share
Report
Share
Recommended
data modeling and models
This document provides an overview of data modeling, including definitions of key concepts like data models and data modeling. It describes the evolution of popular data models from hierarchical to network to relational to entity-relationship to object-oriented models. For each model, it outlines the basic concepts, advantages, and disadvantages. The document emphasizes that newer data models aimed to address shortcomings of previous approaches and capture real-world data and relationships.
Data modelling 101
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
Data models
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
Data Quality
In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
Conceptual Data Modeling
Considering Company as a Mini world Problem we need to design the Database.
Company Database consists of:
Company Info
Employee Info
Project Info
The Formulated Requirements are:
The company is organized into departments. Each department has a unique name, a unique number, and a particular employee who manages the department. We keep track of the start date when that employee began managing the department. A department may have several location
A department controls a number of projects, each of which has a unique name, a unique number, and a single location
We store each employee’s name, Social Security number, address, salary, sex (gender), and birth date. An employee is assigned to one department, but may work on several projects, which are not necessarily controlled by the same department. We keep track of the current number of hours per week that an employee works on each project. We also keep track of the direct supervisor of each employee (who is another employee).
We want to keep track of the dependents of each employee for insurance purposes
Fundamentals of Database ppt ch03
This chapter discusses the relational database model and its basic components. It explains that the relational model provides a logical view of data organized into tables composed of rows and columns. Each row must be uniquely identifiable through a primary key. Tables can be linked together through common attributes, and relationships between entities can be modeled as one-to-one, one-to-many, or many-to-many. The chapter also covers relational operators, keys, data integrity rules, and how to handle data redundancy and indexing in a relational database.
Dimensional Modeling
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
Data base management system
This document provides an overview of database management systems and related concepts. It discusses data hierarchy, traditional file processing, the database approach to data management, features and capabilities of database management systems, database schemas, components of database management systems, common data models including hierarchical, network, and relational models, and the process of data normalization.
Recommended
data modeling and models
This document provides an overview of data modeling, including definitions of key concepts like data models and data modeling. It describes the evolution of popular data models from hierarchical to network to relational to entity-relationship to object-oriented models. For each model, it outlines the basic concepts, advantages, and disadvantages. The document emphasizes that newer data models aimed to address shortcomings of previous approaches and capture real-world data and relationships.
Data modelling 101
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
Data models
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
Data Quality
In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
Conceptual Data Modeling
Considering Company as a Mini world Problem we need to design the Database.
Company Database consists of:
Company Info
Employee Info
Project Info
The Formulated Requirements are:
The company is organized into departments. Each department has a unique name, a unique number, and a particular employee who manages the department. We keep track of the start date when that employee began managing the department. A department may have several location
A department controls a number of projects, each of which has a unique name, a unique number, and a single location
We store each employee’s name, Social Security number, address, salary, sex (gender), and birth date. An employee is assigned to one department, but may work on several projects, which are not necessarily controlled by the same department. We keep track of the current number of hours per week that an employee works on each project. We also keep track of the direct supervisor of each employee (who is another employee).
We want to keep track of the dependents of each employee for insurance purposes
Fundamentals of Database ppt ch03
This chapter discusses the relational database model and its basic components. It explains that the relational model provides a logical view of data organized into tables composed of rows and columns. Each row must be uniquely identifiable through a primary key. Tables can be linked together through common attributes, and relationships between entities can be modeled as one-to-one, one-to-many, or many-to-many. The chapter also covers relational operators, keys, data integrity rules, and how to handle data redundancy and indexing in a relational database.
Dimensional Modeling
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
Data base management system
This document provides an overview of database management systems and related concepts. It discusses data hierarchy, traditional file processing, the database approach to data management, features and capabilities of database management systems, database schemas, components of database management systems, common data models including hierarchical, network, and relational models, and the process of data normalization.
Entity Relationship Diagram
This document discusses entity relationship diagrams (ERDs), which are used to model databases and their logical structure. It defines key terms like entities, attributes, relationships, and cardinality. It explains that ERDs show entities as rectangles, relationships as diamonds, and cardinalities with notation like one-to-one. The document outlines the steps to create an ERD, including identifying entities, attributes, relationships, and cardinalities, then drawing the diagram. It provides examples of one-to-one, one-to-many, and many-to-many relationships.
Data Visualization1.pptx
This document discusses data visualization techniques. It begins by defining data visualization and its importance for analyzing large datasets. It then discusses the advantages of data visualization, including how visuals help people quickly understand trends and outliers. The document also covers the importance of data visualization for business decision making. It lists several benefits, such as enabling better analysis, identifying patterns, and exploring insights. Finally, it categorizes and provides examples of different types of charts for visualizing data, including charts for showing change over time, comparing categories, ranking items, part-to-whole relationships, distributions, flows, and relationships.
Data Warehouse Modeling
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Data models
Introduction of data models ,types of data models ,comparison of hierarchical ,network and relational model
Lecture 01 introduction to database
This document provides an overview of databases and database management systems (DBMS). It discusses how databases evolved from file systems to address flaws in data management. It describes what a DBMS is and its functions in managing the database structure and controlling data access. The document also summarizes different database models including hierarchical, network, relational, entity-relationship, and object-oriented models. It highlights advantages and disadvantages of each model.
Data Quality Strategies
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
How to Strengthen Enterprise Data Governance with Data Quality
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Enterprise Architecture vs. Data Architecture
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
DBMS and its Models
The document discusses database management systems and data modeling. It begins by defining key terms like data, databases, database management systems, and data models. It then provides a brief history of database development from the 1960s to the 1980s. The rest of the document discusses database concepts in more detail, including components of a DBMS, types of database users, database administration responsibilities, data modeling techniques, and the evolution of different data models.
Data Governance Takes a Village (So Why is Everyone Hiding?)
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
Data visualization in a Nutshell
You will learn:
What is Data Visualization?
Data Visualization Framework
3 types of data visualization
Tools (Tableau vs D3.js)
Live Demos
Conceptual vs. Logical vs. Physical Data Modeling
A model is developed for a purpose. Understanding the strengths of each of the three Data Modeling types will prepare you with a more robust analyst toolkit. The program will describe modeling characteristics shared by each modeling type. Using the context of a reverse engineering exercise, delegates will be able to trace model components as they are used in a common data reengineering exercise that is also tied to a Data Governance exercise.
Learning objectives:
-Understanding the role played by models
-Differentiate appropriate use among conceptual, logical, and physical data models
- Understand the rigor of the round-trip data reengineering analyses
- Apply appropriate use of various Data Modeling types
Presentation on Database management system
It includes:
Introduction to Database Management System
DBMS vs File System
View of data
Data models
Database Languages: DML, DDL
Database users and administrators
Transaction Management
Database System Structure
Application architectures
Comparison of Relational Database and Object Oriented Database
The object-oriented database (OODB) is the combination of object-oriented
programming language (OOPL) systems and persistent systems. Object DBMSs add database
functionality to object programming languages. They bring much more than persistent
storage of programming language objects. A major benefit of this approach is the unification
of the application and database development into a seamless data model and language
environment. This report presents the comparison between object oriented database and
relational database. It gives advantages of OODBMS over RDBMS. It gives applications of
OODBMS.
The Importance of Master Data Management
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:
Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Modeling PPT
The document discusses data modeling, which involves creating a conceptual model of the data required for an information system. There are three types of data models - conceptual, logical, and physical. A conceptual data model describes what the system contains, a logical model describes how the system will be implemented regardless of the database, and a physical model describes the implementation using a specific database. Common elements of a data model include entities, attributes, and relationships. Data modeling is used to standardize and communicate an organization's data requirements and establish business rules.
Introduction to Data Visualization
The document provides an introduction and overview of an introductory course on visual analytics. It outlines the course objectives, which include fundamental concepts in data visualization and analysis, exposure to visualization work across different domains, and hands-on experience using data visualization tools. The course covers basic principles of data analysis, perception and design. It includes a survey of visualization examples and teaches students to apply these principles to create their own visualizations. The document also provides a weekly plan that includes topics like data processing, visualization design, cognitive science, and a review of best practices.
Unit1 DBMS Introduction
The document discusses different database concepts:
1) A database is a collection of organized data that can be easily retrieved, inserted, and deleted. Database management systems (DBMS) like MySQL and Oracle are software used to manage databases.
2) The two main data models are the relational model, which organizes data into tables and relations, and the object-oriented model, which represents data as objects with properties and methods.
3) DBMS provide advantages like data sharing, backup/recovery, security, and independence between data and applications. However, they also have disadvantages such as higher costs and complexity.
LDM Webinar: Data Modeling & Metadata Management
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives? Join this webinar to discuss opportunities and challenges around:
- How data modeling fits within a larger metadata management landscape
- When can data modeling provide “just enough” metadata management
- Key data modeling artifacts for metadata
- Organization, Roles & Implementation Considerations
Before you take a gander at particular images, its vital to compre.pdf
Before you take a gander at particular images, it\'s vital to comprehend the different levels of
ERDs. There are a few approaches to model substance relationship outlines. The most abnormal
state sort is a calculated information show; the following most astounding is the coherent
information model, and the least level (and in this manner most point by point) sort is the
physical information model. Counsel the graph beneath to see which components are secured in
every information model.
ER graph images and importance
Calculated DATA MODEL
This ER model builds up an expansive perspective of what ought to be incorporated into the
model set. Applied information models:
Incorporate essential substances and the relationship between them.
Try not to determine properties.
Try not to determine essential keys.
Calculated ERDs can be utilized as the establishment for intelligent information models. They
may likewise be utilized to frame shared trait connections between ER models as a premise for
information model coordination.
Legitimate DATA MODEL
This model contains more detail than the theoretical ER model, without respect to how data will
be physically actualized in the database. Legitimate information models:
Incorporate all substances and connections between them.
Indicate properties for every element.
Indicate essential key for every substance.
Indicate outside keys, which recognize the relationship between various substances.
Include standardization, which is the way toward expelling excess in a table so that the table is
simpler to adjust. Standardization ordinarily happens by partitioning an element table into two or
more tables and characterizing connections between the tables.
PHYSICAL DATA MODEL
The physical information model speaks to the way toward adding data to the database. This
model demonstrates every table structure, including section name, segment information sort,
segment limitations, essential key, outside key, and connections between tables. Physical
information models:
Determine all tables and segments.
Incorporate remote keys to distinguish connections between tables.
May incorporate denormalization, contingent upon client necessities.
May be essentially not quite the same as the intelligent information model.
Will vary contingent upon which DBMS (database administration framework) is utilized.
Applied ERD Symbols
These images are for the most part utilized for reasonable information models, albeit a few
perspectives may overflow into legitimate information models. They can be found in the UML
Entity Relationship and Entity Relationship shape libary of Lucidchart. In the event that you
don\'t see the shape you require, utilize a picture record (Lucidchart bolsters .PNG, .JPG, or
.SVG import) or make your own particular with our current shapes and styling alternatives.
Elements
Elements are items or ideas that speak to critical information. They are normally things, e.g.
client, manager, area, or advancement.
Solid elements exi.
Data modeling levels and techniques.docx
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.
More Related Content
What's hot
Entity Relationship Diagram
This document discusses entity relationship diagrams (ERDs), which are used to model databases and their logical structure. It defines key terms like entities, attributes, relationships, and cardinality. It explains that ERDs show entities as rectangles, relationships as diamonds, and cardinalities with notation like one-to-one. The document outlines the steps to create an ERD, including identifying entities, attributes, relationships, and cardinalities, then drawing the diagram. It provides examples of one-to-one, one-to-many, and many-to-many relationships.
Data Visualization1.pptx
This document discusses data visualization techniques. It begins by defining data visualization and its importance for analyzing large datasets. It then discusses the advantages of data visualization, including how visuals help people quickly understand trends and outliers. The document also covers the importance of data visualization for business decision making. It lists several benefits, such as enabling better analysis, identifying patterns, and exploring insights. Finally, it categorizes and provides examples of different types of charts for visualizing data, including charts for showing change over time, comparing categories, ranking items, part-to-whole relationships, distributions, flows, and relationships.
Data Warehouse Modeling
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Data models
Introduction of data models ,types of data models ,comparison of hierarchical ,network and relational model
Lecture 01 introduction to database
This document provides an overview of databases and database management systems (DBMS). It discusses how databases evolved from file systems to address flaws in data management. It describes what a DBMS is and its functions in managing the database structure and controlling data access. The document also summarizes different database models including hierarchical, network, relational, entity-relationship, and object-oriented models. It highlights advantages and disadvantages of each model.
Data Quality Strategies
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
How to Strengthen Enterprise Data Governance with Data Quality
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Enterprise Architecture vs. Data Architecture
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
DBMS and its Models
The document discusses database management systems and data modeling. It begins by defining key terms like data, databases, database management systems, and data models. It then provides a brief history of database development from the 1960s to the 1980s. The rest of the document discusses database concepts in more detail, including components of a DBMS, types of database users, database administration responsibilities, data modeling techniques, and the evolution of different data models.
Data Governance Takes a Village (So Why is Everyone Hiding?)
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
Data visualization in a Nutshell
You will learn:
What is Data Visualization?
Data Visualization Framework
3 types of data visualization
Tools (Tableau vs D3.js)
Live Demos
Conceptual vs. Logical vs. Physical Data Modeling
A model is developed for a purpose. Understanding the strengths of each of the three Data Modeling types will prepare you with a more robust analyst toolkit. The program will describe modeling characteristics shared by each modeling type. Using the context of a reverse engineering exercise, delegates will be able to trace model components as they are used in a common data reengineering exercise that is also tied to a Data Governance exercise.
Learning objectives:
-Understanding the role played by models
-Differentiate appropriate use among conceptual, logical, and physical data models
- Understand the rigor of the round-trip data reengineering analyses
- Apply appropriate use of various Data Modeling types
Presentation on Database management system
It includes:
Introduction to Database Management System
DBMS vs File System
View of data
Data models
Database Languages: DML, DDL
Database users and administrators
Transaction Management
Database System Structure
Application architectures
Comparison of Relational Database and Object Oriented Database
The object-oriented database (OODB) is the combination of object-oriented
programming language (OOPL) systems and persistent systems. Object DBMSs add database
functionality to object programming languages. They bring much more than persistent
storage of programming language objects. A major benefit of this approach is the unification
of the application and database development into a seamless data model and language
environment. This report presents the comparison between object oriented database and
relational database. It gives advantages of OODBMS over RDBMS. It gives applications of
OODBMS.
The Importance of Master Data Management
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:
Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Modeling PPT
The document discusses data modeling, which involves creating a conceptual model of the data required for an information system. There are three types of data models - conceptual, logical, and physical. A conceptual data model describes what the system contains, a logical model describes how the system will be implemented regardless of the database, and a physical model describes the implementation using a specific database. Common elements of a data model include entities, attributes, and relationships. Data modeling is used to standardize and communicate an organization's data requirements and establish business rules.
Introduction to Data Visualization
The document provides an introduction and overview of an introductory course on visual analytics. It outlines the course objectives, which include fundamental concepts in data visualization and analysis, exposure to visualization work across different domains, and hands-on experience using data visualization tools. The course covers basic principles of data analysis, perception and design. It includes a survey of visualization examples and teaches students to apply these principles to create their own visualizations. The document also provides a weekly plan that includes topics like data processing, visualization design, cognitive science, and a review of best practices.
Unit1 DBMS Introduction
The document discusses different database concepts:
1) A database is a collection of organized data that can be easily retrieved, inserted, and deleted. Database management systems (DBMS) like MySQL and Oracle are software used to manage databases.
2) The two main data models are the relational model, which organizes data into tables and relations, and the object-oriented model, which represents data as objects with properties and methods.
3) DBMS provide advantages like data sharing, backup/recovery, security, and independence between data and applications. However, they also have disadvantages such as higher costs and complexity.
LDM Webinar: Data Modeling & Metadata Management
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives? Join this webinar to discuss opportunities and challenges around:
- How data modeling fits within a larger metadata management landscape
- When can data modeling provide “just enough” metadata management
- Key data modeling artifacts for metadata
- Organization, Roles & Implementation Considerations
What's hot (20)
How to Strengthen Enterprise Data Governance with Data Quality
How to Strengthen Enterprise Data Governance with Data Quality
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...
Data Governance Takes a Village (So Why is Everyone Hiding?)
Data Governance Takes a Village (So Why is Everyone Hiding?)
Comparison of Relational Database and Object Oriented Database
Comparison of Relational Database and Object Oriented Database
Similar to The three level of data modeling
Before you take a gander at particular images, its vital to compre.pdf
Before you take a gander at particular images, it\'s vital to comprehend the different levels of
ERDs. There are a few approaches to model substance relationship outlines. The most abnormal
state sort is a calculated information show; the following most astounding is the coherent
information model, and the least level (and in this manner most point by point) sort is the
physical information model. Counsel the graph beneath to see which components are secured in
every information model.
ER graph images and importance
Calculated DATA MODEL
This ER model builds up an expansive perspective of what ought to be incorporated into the
model set. Applied information models:
Incorporate essential substances and the relationship between them.
Try not to determine properties.
Try not to determine essential keys.
Calculated ERDs can be utilized as the establishment for intelligent information models. They
may likewise be utilized to frame shared trait connections between ER models as a premise for
information model coordination.
Legitimate DATA MODEL
This model contains more detail than the theoretical ER model, without respect to how data will
be physically actualized in the database. Legitimate information models:
Incorporate all substances and connections between them.
Indicate properties for every element.
Indicate essential key for every substance.
Indicate outside keys, which recognize the relationship between various substances.
Include standardization, which is the way toward expelling excess in a table so that the table is
simpler to adjust. Standardization ordinarily happens by partitioning an element table into two or
more tables and characterizing connections between the tables.
PHYSICAL DATA MODEL
The physical information model speaks to the way toward adding data to the database. This
model demonstrates every table structure, including section name, segment information sort,
segment limitations, essential key, outside key, and connections between tables. Physical
information models:
Determine all tables and segments.
Incorporate remote keys to distinguish connections between tables.
May incorporate denormalization, contingent upon client necessities.
May be essentially not quite the same as the intelligent information model.
Will vary contingent upon which DBMS (database administration framework) is utilized.
Applied ERD Symbols
These images are for the most part utilized for reasonable information models, albeit a few
perspectives may overflow into legitimate information models. They can be found in the UML
Entity Relationship and Entity Relationship shape libary of Lucidchart. In the event that you
don\'t see the shape you require, utilize a picture record (Lucidchart bolsters .PNG, .JPG, or
.SVG import) or make your own particular with our current shapes and styling alternatives.
Elements
Elements are items or ideas that speak to critical information. They are normally things, e.g.
client, manager, area, or advancement.
Solid elements exi.
Data modeling levels and techniques.docx
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.
Data modeling levels and techniques.docx
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.
Data modeling levels and techniques.docx
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.
1.1 Data Modelling - Part I (Understand Data Model).pdf
Data modeling is the process of creating a data model for data stored in a database. It ensures consistency in naming conventions, default values, semantics, and security while also ensuring data quality. There are three main types of data models: conceptual, logical, and physical. The conceptual model establishes entities, attributes, and their relationships. The logical model defines data element structure and relationships. The physical model describes database-specific implementation. The primary goal is accurately representing required data objects. Drawbacks include requiring application modifications for even small structure changes and lacking a standard data manipulation language.
Data Modeling.docx
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.
database-model group presentation.pptx
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.
Student POST Database processing models showcase the logical s.docx
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 .
2. Chapter Two.pdf
The document discusses database system architecture and data models. It introduces the three schema architecture which separates the conceptual, logical and internal schemas. This provides logical data independence where the conceptual schema can change without affecting external schemas or applications. It also discusses various data models like hierarchical, network, relational and object-oriented models. Key aspects of each model like structure, relationships and operations are summarized.
Bca examination 2017 dbms
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
DBMS OF DATA MODEL Deepika 2
This document provides an overview of different data models, including object-based models like the entity-relationship model and object-oriented model, and record-based models like the relational, network, and hierarchical models. It describes the key features of each model, such as how data and relationships are represented, and highlights some of their advantages and disadvantages. The presentation aims to guide students in understanding different approaches to database design and modeling.
Database System Concepts AND architecture [Autosaved].pptx
The document discusses key concepts in database systems and architecture. It defines what a data model is and describes different types of data models including high-level conceptual models, low-level physical models, and representation models. It also outlines several common data models like hierarchical, network, relational, object-oriented, and object-relational models. Finally, it provides a simplified overview of the key components of a database system, including the database, DBMS software, catalog, and users/applications.
DBMS
This document provides an overview of different data models, including object-based models like the entity-relationship model and object-oriented model, and record-based models like the relational, network, and hierarchical models. It describes the key features of each model, such as how data and relationships are represented, and highlights some advantages and disadvantages. The presentation is intended to guide students in understanding different approaches to database design and logical data modeling.
Data models
The document discusses several data models including flat file, hierarchical, network, relational, object-relational, and object-based models. It provides details on the flat file model, describing it as a single two-dimensional array containing data elements in columns and related elements in rows. The object-relational model combines relational and object-oriented features, allowing integration of databases with object-oriented data types and methods. The document also discusses the entity-relationship model, which is an object-based logical model that uses entities, attributes, and relationships to flexibly structure data and specify constraints.
Data models
The document discusses several data models including flat file, hierarchical, network, relational, object-relational, and object-based models. It provides details on the flat file model, describing it as a single two-dimensional array containing data elements in columns and related elements in rows. The object-relational model combines relational and object-oriented features, allowing integration of complex data types. The object-based model uses entities, attributes, and relationships, with the entity-relationship model being a commonly used object-based logical model.
chapter 2-DATABASE SYSTEM CONCEPTS AND architecture [Autosaved].pdf
This document discusses database system concepts and architecture. It covers topics such as data models, schemas, and the three-schema architecture. Specifically, it describes different data models including hierarchical, network, relational, entity-relationship, and object-oriented models. It also distinguishes between database schemas, which define the structure and constraints of a database, and database instances, which represent the actual stored data.
Data modeling 101 - Basics - Software Domain
The document provides an overview of data modeling. It defines data modeling as creating conceptual representations of data objects and their relationships. The key points covered include:
- Data modeling involves multiple steps like requirements gathering, conceptual design, logical design, and physical design.
- It describes different levels of abstraction including conceptual, logical, and physical levels.
- Examples of different data modeling techniques are provided, such as ER modeling, hierarchical modeling, network modeling, relational modeling, object-oriented modeling, and object-relational modeling.
- Benefits of data modeling include improved understanding of data, improved data quality, and increased efficiency. Limitations include potential lack of flexibility and complexity.
- The significance
Data Modeling Training.pptx
Data Modeling Training.pptxData Modeling Training.pptxData Modeling Training.pptxData Modeling Training.pptx
Dbms unit i
The document discusses key concepts related to database management systems (DBMS). It defines a database as a collection of related data used to solve an institution's data management needs. A DBMS is software that allows users to define, create, maintain and control access to the database. The document outlines the differences between data and databases, as well as the characteristics and components of a DBMS, including different views (physical, conceptual, external) of databases. It also discusses data modeling concepts such as entities, attributes, keys, and different types of data models (conceptual, logical, physical).
activity 1 for module 2.docx
The document discusses key concepts in data modeling including:
1) The importance of data modeling in creating a logical database that reduces redundancy and enables efficient data retrieval.
2) How business rules are translated into data model components such as entities, attributes, and relationships.
3) The emergence of standard languages like DML and DLL which helped standardize early network data models.
Similar to The three level of data modeling (20)
Before you take a gander at particular images, its vital to compre.pdf
Before you take a gander at particular images, its vital to compre.pdf
1.1 Data Modelling - Part I (Understand Data Model).pdf
1.1 Data Modelling - Part I (Understand Data Model).pdf
Student POST Database processing models showcase the logical s.docx
Student POST Database processing models showcase the logical s.docx
Database System Concepts AND architecture [Autosaved].pptx
Database System Concepts AND architecture [Autosaved].pptx
chapter 2-DATABASE SYSTEM CONCEPTS AND architecture [Autosaved].pdf
chapter 2-DATABASE SYSTEM CONCEPTS AND architecture [Autosaved].pdf
Recently uploaded
OpenID AuthZEN Interop Read Out - Authorization
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Recently uploaded (20)
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
The three level of data modeling
- 1. The three level of data modeling, conceptual data model, logical data model, and physical data model, were discussed in prior sections. Here we compare these three types of data models. The table below compares the different features: Feature Conceptual Logical Physical Entity Names ✓ ✓ Entity Relationships ✓ ✓ Attributes ✓ Primary Keys ✓ ✓ Foreign Keys ✓ ✓ Table Names ✓ Column Names ✓ Column Data Types ✓ Below we show the conceptual, logical, and physical versions of a single data model. Conceptual Model Design Logical Model Design Physical Model Design We can see that the complexity increases from conceptual to logical to physical. This is why we always first start with the conceptual data model (so we understand at high level what are the different entities in our data and how they relate to one another), then move on to the logical data model (so we
- 2. understand the details of our data without worrying about how they will actually implemented), and finally the physical data model (so we know exactly how to implement our data model in the database of choice). In a data warehousing project, sometimes the conceptual data model and the logical data model are considered as a single deliverable. A Conceptual Data Model A conceptual data model identifies the highest-level relationships between the different entities. Features of conceptual data model include: Includes the important entities and the relationships among them. No attribute is specified. No primary key is specified. The figure below is an example of a conceptual data model. Conceptual Data Model From the figure above, we can see that the only information shown via the conceptual data model is the entities that describe the data and the relationships between those entities. No other information is shown through the conceptual data model.
- 3. A Logical Data Model A logical data model describes the data in as much detail as possible, without regard to how they will be physical implemented in the database. Features of a logical data model include: Includes all entities and relationships among them. All attributes for each entity are specified. The primary key for each entity is specified. Foreign keys (keys identifying the relationship between different entities) are specified. Normalization occurs at this level. The steps for designing the logical data model are as follows: 1. Specify primary keys for all entities. 2. Find the relationships between different entities. 3. Find all attributes for each entity. 4. Resolve many-to-many relationships. 5. Normalization. The figure below is an example of a logical data model.
- 4. Logical Data Model Comparing the logical data model shown above with the conceptual data model diagram, we see the main differences between the two: In a logical data model, primary keys are present, whereas in a conceptual data model, no primary key is present. In a logical data model, all attributes are specified within an entity. No attributes are specified in a conceptual data model. Relationships between entities are specified using primary keys and foreign keys in a logical data model. In a conceptual data model, the relationships are simply stated, not specified, so we simply know that two entities are related, but we do not specify what attributes are used for this relationship.
- 5. A Physical Data Model Physical data model represents how the model will be built in the database. A physical database model shows all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between tables. Features of a physical data model include: Specification all tables and columns. Foreign keys are used to identify relationships between tables. Denormalization may occur based on user requirements. Physical considerations may cause the physical data model to be quite different from the logical data model. Physical data model will be different for different RDBMS. For example, data type for a column may be different between MySQL and SQL Server. The steps for physical data model design are as follows: 1. Convert entities into tables. 2. Convert relationships into foreign keys. 3. Convert attributes into columns. 4. Modify the physical data model based on physical constraints / requirements. The figure below is an example of a physical data model. Physical Data Model
- 6. Comparing the logical data model shown above with the logical data model diagram, we see the main differences between the two: Entity names are now table names. Attributes are now column names. Data type for each column is specified. Data types can be different depending on the actual database being used.