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 modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
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
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.
Advanced Database Systems CS352Unit 2 Individual Project.docxnettletondevon
Advanced Database Systems CS352
Unit 2 Individual Project
Randle Kuhn
02/29/16
Contents
The Database Models, Languages, and Architecture 3
Database System Development Life Cycle 6
Database Management Systems 9
Advanced SQL 10
Web and Data Warehousing and Mining in the Business World 11
References 12
The Database Models, Languages, and Architecture
It is exceedingly essential for every organization to evaluate its constituent database needs/requirements so as to determine whether it will be operationally compatible with the distinct architectural layouts available. Making the wrong choice of architectural design results to degraded database performance in terms of speed of accessing data as well as executing data definition and manipulation commands. These architectural database designs include the 3-level architecture which is implemented under the ANSI-SPARC (American National Standards Institute, Standards Planning and Requirements Committee) architectural framework of computational standards. It was inaugurated in the year 1975 as an abstract standard for utilization in DBMSs (Database Management System). The core objective of this 3-level architecture is to introduce efficient database operability by separating the users view from the other views (internal, conceptual and external). The user’s view is implemented and operates independently of the underlying database architecture. Therefore, multiple users are able to access similar data items synchronously while at the same time customizing their respective views with no regard to the other users’ views (www.computingstudents.com, 2009). Additionally, it ensures that the users are not presented with the sophisticated hardware/physical implementation details which are basically irrelevant to users. The access speed for this type of architecture is exceedingly high with fault tolerance capabilities.
Data independence refers to a very important concept utilized in centrally oriented database management systems and which incorporates data transparency. This sort of transparency exempts the users from being affected by any alterations conducted on the structural or organizational makeup of the underlying data. According to the guidelines followed by data independence policies, the user applications should not be involved in problems or issues emanating from the internal data definitions. Operations conducted by the user applications should not be influenced in any way by these internal data modifications (Zaiane, 2016). Data independence is subdivided into two categories namely first level and second level of data independence.
Data administrators are responsible of many essential roles which are different from those of a database administrator in several ways. For instance, a data administrator is in charge of coming up with the necessary definition of data items, creating names to refer to various data items as well as their respective relationships. He/she often consult datab.
Is 581 milestone 7 and 8 case study coastline systems consultingprintwork4849
IS 581 Milestone 7 and 8 Case study Coastline Systems Consulting
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IS 581 Milestone 3 and 4 Case study Coastline Systems Consulting
IS 581 Milestone 1 and 2 Case study Coastline Systems Consulting
IS 581 Milestone 9 and 10 Case study Coastline Systems Consulting
IS 581 Milestone 11 and 12 Case study Coastline Systems Consulting
● Distributed Database Management Systems Advantages and Disadvantages.
● Characteristics of Distributed Database Management Systems.
● Levels of Data and Process Distribution.
● Distributed Database Transparency Features.
● Transaction Performance and Failure Transparency.
● Why Databases?
● Why Database Design is Important?
● The Database System Environment and Functions.
● Managing the Database System: A Shift in Focus.
Data Science is a field where we apply 'science' to available 'data' in order to get the 'patterns' or 'insights' which can help a business to optimize operations or improvise decisions.
In this PPT, you will learn:
• The difference between data and information
• What a database is, the various types of databases, and why they are valuable assets for
decision making
• The importance of database design
• How modern databases evolved from file systems
• About flaws in file system data management
• The main components of the database system
• The main functions of a database management system (DBMS)
E-marketing is a process of planning and executing the conception, distribution, promotion, and pricing of products and services in a computerized, networked environment, such as the Internet and the World Wide Web, to facilitate exchanges and satisfy customer demands.
ERP modules and business software packageUsman Tariq
In organization, ERP helps to manage business processes of various departments & functions through centralized application. We can make all the major decisions by screening the information provided by ERP.
There are many vendors in market which are providing traditional ERP solutions or Cloud based ERP solutions. Though implementation platforms or technologies are different, there are common & basic modules of ERP which can be found in any ERP System. Depending on organizations need required components are integrated & customized ERP system is formed. All the below mentioned modules can be found in any ERP system:
• Human Resource, Inventory, Sales & Marketing, Purchase, Finance & Accounting, Customer Relationship Management (CRM), Engineering/ Production, Supply Chain Management (SCM)
Each component mentioned above is specialized to handle defined business processes of organization. Let us go through the introduction of the various modules.
ERP Implementation Challenges and Package SelectionUsman Tariq
ERP implementations have a nasty reputation for being challenging.
These challenges can lead to your ERP implementation project taking too much time and being over budget.
The result can be you being left with an underperforming solution. Or, you avoiding implementation of an ERP at all costs.
While the challenges are real, they shouldn’t stop you from implementing one.
Discuss overall trends in Internet access, usage, and purchasing around the world.
Define emerging economies and explain the vital role of information technology in economic development.
Outline how e-marketers apply market similarity and analyze online purchase and payment behaviors in planning market entry opportunities.
Deploying an enterprise resource planning (ERP) system is an expensive proposition, not just in terms of licensing and maintenance, but in terms of dedicated resources and time. The implementation of ERP systems has helped small and mid-sized companies, significantly improve their business metrics by process optimization, improving the entire supply chain process, better inventory control, better reporting to take decisions, integration across functionalities and increasing transparency across the company. Purchase department can see the sales department data, Sales department can see inventory data, and top management can see any data on a click of single button.
Customer Relationship Management (CRM) is a strategy for managing all your company’s relationships and interactions with customers and potential customers. It helps you stay connected to them, streamline processes and improve your profitability.
More commonly, when people talk about CRM they are usually referring to a CRM system, a tool which helps with contact management, sales management, productivity and more.
Customer Relationship Management enables you to focus on your organization’s relationships with individual people – whether those are customers, service users, colleagues or suppliers. CRM is not just for sales. Some of the biggest gains in productivity can come from moving beyond CRM as a sales and marketing tool and embedding it in your business – from HR to customer services and supply-chain management.
A marketing plan should be a formal written document, not recalled from memory or something scribbled on a napkin. To take your business to the next level requires preparing a written marketing action plan.
Strategic E-Marketing and Performance MetricsUsman Tariq
E-marketing means using digital technologies such as websites, mobile devices and social networking to help reach your customers, create awareness of your brand and sell your goods or services. The basics of marketing remain the same - creating a strategy to deliver the right messages to the right people.
ERP is an acronym for Enterprise Resource Planning, but even its full name doesn't shed much light on what ERP is or what it does. For that, you need to take a step back and think about all of the various processes that are essential to running a business, including inventory and order management, accounting, human resources, customer relationship management (CRM), and beyond. At its most basic level, ERP software integrates these various functions into one complete system to streamline processes and information across the entire organization.
The central feature of all ERP systems is a shared database that supports multiple functions used by different business units. In practice, this means that employees in different divisions—for example, accounting and sales—can rely on the same information for their specific needs.
E-Marketing (past, present, and future)Usman Tariq
Textbook Title: “E-Marketing, 8th E": international Edition
Author: Raymond D. Frost, Alexa Fox, Judy Strauss
Publisher: Taylor & Francis, ISBN-13: 9781138731370
Year/Edition: 8th ed. was published by Rutledge
Publication Date: 2018-10-18
ERP integrates business of an organization through a centralized database. The organizational data and transaction data are stored in the database. This data is a rich source of information. There are many software tools that would process the data and discover useful patterns. These techniques are referred to as data mining. The data from an ERP system may not be directly usable by data mining tools. The data may have to be pre-processed and made ready for data mining. A data warehouse is created from the ERP data that makes the data ready for data mining. An organization needs to interact with their suppliers for obtaining the raw material or semi-finished goods. They also need to interact with their retailers and dealers. These interactions may happen using EDI technology. Supply chain management (SCM) refers to managing suppliers and retailers. Customers are the reason why a business exists. The focus has changed from providing customer a product to providing a service built around the product. Customer relationship management (CRM) is the technology that helps an organization to manage its customers. CRM and SCM both integrate with ERP system and are collectively referred to as ERP-II.
In the computer industry, an enterprise is an organization that uses computers. A word was needed that would encompass corporations, small businesses, non-profit institutions, government bodies, and possibly other kinds of organizations. The term enterprise seemed to do the job. In practice, the term is applied much more often to larger organizations than smaller ones.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
1. Chapter 2
Data Models
ISBN-13: 978-1337627900
ISBN-10: 1337627909
Buy Book Amazon.com URL
Modified by:
Usman Tariq, PhD
Associate Professor, PSAU
Office ☎ 00966 11 588 8386
2. Learning Objectives
In this chapter, 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
2
3. Learning Objectives
In this chapter, you will learn:
How the major data models evolved
About emerging alternative data models and the need
they fulfill
How data models can be classified by their level of
abstraction
3
4. Data Modeling and Data Models
• Data modeling: It is a process of creating a data model
for the data to be stored in a Database i.e. a conceptual
representation of
• Data objects
• The associations between different data objects
• The rules.
Data modeling helps in the visual representation of data
and enforces business rules, regulatory compliances, and
government policies on the data. Data Models ensure
consistency in naming conventions, default values,
semantics, security while ensuring quality of the data.
4
5. Data Modeling and Data Models
Data model emphasizes on (1) what data is needed and
(2) how it should be organized instead of what
operations need to be performed on the data.
Data models: Simple representations of complex
real-world data structures
Useful for supporting a specific problem domain
Model - Abstraction of a real-world object or event
5
6. Why use Data Model?
The primary goal of using data model are:
1. Ensures that all data objects required by the database are accurately
represented. Omission of data will lead to creation of faulty reports and
produce incorrect results.
2. A data model helps design the database at the conceptual, physical and
logical levels.
3. Data Model structure helps to define the relational tables, primary and
foreign keys and stored procedures.
4. It provides a clear picture of the base data and can be used by database
developers to create a physical database.
5. It is also helpful to identify missing and redundant data.
6. Though the initial creation of data model is labor and time consuming, in
the long run, it makes your IT infrastructure upgrade and maintenance
cheaper and faster. 6
7. Types of Data Models [1/2]
Conceptual: This Data Model defines WHAT the system
contains. This model is typically created by Business
stakeholders and Data Architects. The purpose is to organize,
scope and define business concepts and rules.
Logical: Defines HOW the system should be implemented
regardless of the DBMS. This model is typically created by
Data Architects and Business Analysts. The purpose is to
developed technical map of rules and data structures.
Physical: This Data Model describes HOW the system will be
implemented using a specific DBMS system. This model is
typically created by DBA and developers. The purpose is
actual implementation of the database.
7
9. Advantages of Data model
The main goal of a designing data model is to make certain that
data objects offered by the functional team are represented
accurately.
The data model should be detailed enough to be used for building
the physical database.
The information in the data model can be used for defining the
relationship between tables, primary and foreign keys, and stored
procedures.
Data Model helps business to communicate the within and across
organizations.
Data model helps to documents data mappings in ETL process
Help to recognize correct sources of data to populate the model
9
10. Importance of Data Models
Are a communication tool
Give an overall view of the database
Organize data for various users
Are an abstraction for the creation of good
database
10
11. Disadvantages of Data model
To develop Data model one should know
physical data stored characteristics.
This is a navigational system produces
complex application development,
management. Thus, it requires a
knowledge of the biographical truth.
Even smaller change made in structure
require modification in the entire
application.
There is no set data manipulation
language in DBMS.
11Space in a Data File
12. Data Model Basic Building Blocks
Entity: Unique and distinct object used to collect
and store data
Attribute: Characteristic of an entity
Relationship: Describes an association among
entities
One-to-many (1:M)
Many-to-many (M:N or M:M)
One-to-one (1:1)
Constraint: Set of rules to ensure data integrity
12
13. Business Rules
Brief, precise, and unambiguous description of a
policy, procedure, or principle
Enable defining the basic building blocks
Describe main and distinguishing characteristics
of the data
13
17. Ask yourself the following questions,
before any modification
1. Will this rule be violated if I enter a new record into this
table?
2. Will this rule be violated if I do not enter a new record
into this table?
3. Will this rule be violated if I delete a record from this
table?
4. Will this rule be violated if I enter a value into this field?
5. Will this rule be violated if I do not enter a value into this
field?
6. Will this rule be violated if I update the value of this field?
7. Will this rule be violated if I delete the value of this field?
17
18. Business Rules are normalized to represent the
desired business level semantics
18
20. Sources of Business Rules
Company
managers
Policy makers
Department
managers
Written
documentation
Direct
interviews
with end users
20
21. Reasons for Identifying and Documenting
Business Rules
Help standardize company’s view of data
Communications tool between users and designers
Allow designer to:
Understand the nature, role, scope of data, and business
processes
Develop appropriate relationship participation rules and
constraints
Create an accurate data model
21
22. Translating Business Rules into ‘Data
Model Components’
Nouns translate into entities
Verbs translate into relationships among entities
Relationships are bidirectional
Questions to identify the relationship type
How many instances of ‘B’ are related to one instance
of ‘A’?
How many instances of ‘A’ are related to one instance
of ‘B’?
22
23. Naming Conventions
Entity names - Required to:
Be descriptive of the objects in the business
environment
Use terminology that is familiar to the users
Attribute name - Required to be descriptive of the
data represented by the attribute
Proper naming:
Facilitates communication between parties
Promotes self-documentation
23
24. Database Naming Conventions Best Practices
1. Consistency is always the best policy.
2. Every table should have its own row identifier
3. Plural or singular names don’t really matter
4. Never allow the database to put in the constraint
names automatically
5. Avoid being redundant so you can avoid being
redundant
24
25. Hierarchical and Network Models
Hierarchical Models Network Models
Manage large amounts of data
for complex manufacturing
projects
Represented by an upside-
down tree which contains
segments
Segments: Equivalent of a file
system’s record type
Depicts a set of one-to-many
(1:M) relationships
Represent complex data
relationships
Improve database performance
and impose a database
standard
Depicts both one-to-many
(1:M) and many-to-many
(M:N) relationships
25
26. Hierarchical Model
Advantages Disadvantages
Promotes data sharing
Parent/child relationship promotes
conceptual simplicity and data
integrity
Database security is provided and
enforced by DBMS
Efficient with 1:M relationships
Requires knowledge of physical
data storage characteristics
Navigational system requires
knowledge of hierarchical path
Changes in structure require
changes in all application
programs
Implementation limitations
No data definition
Lack of standards
26
27. Network Model
Advantages Disadvantages
Conceptual simplicity
Handles more relationship types
Data access is flexible
Data owner/member relationship
promotes data integrity
Conformance to standards
Includes data definition language
(DDL) and data manipulation
language (DML)
System complexity limits
efficiency
Navigational system yields
complex implementation,
application development, and
management
Structural changes require
changes in all application
programs
27
28. Standard Database Concepts
28
Schema
Conceptual organization of the entire database as
viewed by the database administrator
To add a new entity attribute in the relational model, you
need to modify the table definition. To add a new attribute
in the key-value store, you add a row to the key-value
store, which is why it is said to be “schema-less.”
Schema is of three types: Physical schema, logical
schema and view schema.
The design of a database at physical level is called physical schema. It describes how the data
stored on the disk or the physical storage.
Design of database at logical level is called logical schema, programmers and database
administrator work at this level. At this level data can be described as certain types of data
records gets stored in data structures.
Design of database at view level is called view schema. This generally describes end user
interaction with database systems.
29. Standard Database Concepts
Subschema
Portion of the database seen by the application programs that produce
the desired information from the data within the database
A subschema provides a view of the database as seen by an
application program.
This view is often a subset of the complete schema definition.
A subschema is used at run time to provide the DBMS with a
description of those portions of the database that are accessible to the
application program.
The subschema allows the user to view only that part of the
database that is of interest to him.
The subschema defines the portion of the database as seen by the
application programs and the application programs can have different
view of data stored in the database. 29
30. Standard Database Concepts
30
Data manipulation language (DML)
Environment in which data can be managed and is used to work with the
data in the database
SQL includes commands to insert, update, delete, and retrieve data within
the database tables.
PL/SQL blocks can contain only standard SQL data manipulation language
(DML) commands such as SELECT, INSERT, UPDATE, and DELETE.
The use of data definition language (DDL) commands is not directly
supported in a PL/SQL block.
31. Standard Database Concepts
Schema data definition language (DDL)
Enables the database administrator to define the schema
components
DDL allows a database administrator to define the
database structure, schema, and subschema.
Sub-Schema DDL, allows application programs to
define the database components that will be used.
31
32. Standard Database Concepts
Schema data definition language
Data Definition allows the specification of not only a
set of relations but also information about each relation,
including:
The schema for each relation.
The domain of values associated with each attribute.
Integrity constraints
The set of indices to be maintained for each relations.
Security and authorization information for each relation.
The physical storage structure of each relation on disk.
32
33. Standard Database Concepts
Schema data definition language (DDL) Commands
CREATE
ALTER
The Oracle ALTER TABLE statement is used to add, modify, or drop/delete columns
in a table. The Oracle ALTER TABLE statement is also used to rename a table.
DROP
To remove a relation from an SQL database, we use the drop table command. The drop
table command deletes all information about the dropped relation from the database.
The command
drop table r 33
34. The Relational Model
Produced an automatic transmission database that
replaced standard transmission databases
Based on a relation
Relation or table: Matrix composed of intersecting
tuple and attribute
Tuple: Rows
Attribute: Columns
Describes a precise set of data manipulation
constructs
34
35. Relational Model
Advantages Disadvantages
Structural independence is
promoted using independent
tables
Tabular view improves
conceptual simplicity
Ad hoc query capability is based
on SQL
Isolates the end user from
physical-level details
Improves implementation and
management simplicity
Requires substantial hardware and
system software overhead
Conceptual simplicity gives
untrained people the tools to use a
good system poorly
May promote information
problems
35
36. Relational Database Management System(RDBMS)
Performs basic functions provided by the hierarchical
and network DBMS systems
Makes the relational data model easier to understand
and implement
Hides the complexities of the relational model from
the user
36
38. SQL-Based Relational Database
Application
End-user interface
Allows end user to interact with the data
Collection of tables stored in the database
Each table is independent from another
Rows in different tables are related based on common
values in common attributes
SQL engine
Executes all queries
38
39. The Entity Relationship Model
Graphical representation of entities and their
relationships in a database structure
Entity relationship diagram (ERD)
Uses graphic representations to model database
components
Entity instance or entity occurrence
Rows in the relational table
Connectivity: Term used to label the relationship
types
39
40. Entity Relationship Model
Advantages Disadvantages
Visual modeling yields
conceptual simplicity
Visual representation makes it
an effective communication
tool
Is integrated with the dominant
relational model
Limited constraint
representation
Limited relationship
representation
No data manipulation
language
Loss of information content
occurs when attributes are
removed from entities to avoid
crowded displays
40
42. The Object-Oriented Data Model (OODM)
or Semantic Data Model
Object-oriented database management system
(OODBMS)
Based on OODM
Object: Contains data and their relationships with
operations that are performed on it
Basic building block for autonomous structures
Abstraction of real-world entity
Attributes - Describe the properties of an object
42
43. The Object-Oriented Data Model (OODM)
Class: Collection of similar objects with shared
structure and behavior organized in a class hierarchy
Class hierarchy: Resembles an upside-down tree in
which each class has only one parent
Inheritance: Object inherits methods and attributes
of parent class
Unified Modeling Language (UML)
Describes sets of diagrams and symbols to graphically
model a system
43
44. Object-Oriented Model
Advantages Disadvantages
Semantic content is added
Visual representation includes
semantic content
Inheritance promotes data
integrity
Slow development of
standards caused vendors to
supply their own
enhancements
Compromised widely accepted
standard
Complex navigational system
Learning curve is steep
High system overhead slows
transactions
44
45. Figure 2.4 - A Comparison of OO, UML,
and ER Models
45
46. Object/Relational and XML
Extended relational data model (ERDM)
Supports OO features and complex data
representation
Object/Relational Database Management System
(O/R DBMS)
Based on ERDM, focuses on better data management
Extensible Markup Language (XML)
Manages unstructured data for efficient and
effective exchange of all data types
46
47. Big Data
Aims to:
Find new and better ways to manage large amounts of
web and sensor-generated data
Provide high performance and scalability at a
reasonable cost
Characteristics
Volume
Velocity
Variety
47
48. Different formats of ‘big data’
The three different formats of big data are:
Structured: Organized data format with a fixed
schema. Ex: RDBMS
Semi-Structured: Partially organized data which does
not have a fixed format. Ex: XML, JSON
Unstructured: Unorganized data with an unknown
schema. Ex: Audio, video files etc.
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49. Characteristics of Big Data
Validity: correctness of data
Variability: dynamic behavior
Volatility: tendency to change in time
Vulnerability: vulnerable to breach or attacks
Visualization: visualizing meaningful usage of data
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50. Big Data Challenges
Volume does not allow the usage of
conventional structures
Expensive
OLAP tools proved inconsistent dealing
with unstructured data
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51. Big Data New Technologies
Hadoop
Hadoop Distributed
File System (HDFS)
MapReduce NoSQL
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53. Big Data Applications
• Entertainment: Netflix and Amazon use Big Data to make shows and movie
recommendations to their users.
• Insurance: Uses Big data to predict illness, accidents and price their products
accordingly.
• Driver-less Cars: Google’s driver-less cars collect about one gigabyte of data per
second. These experiments require more and more data for their successful execution.
• Education: Opting for big data powered technology as a learning tool instead of
traditional lecture methods, which enhanced the learning of students as well aided the
teacher to track their performance better.
• Automobile: Rolls Royce has embraced Big Data by fitting hundreds of sensors into its
engines and propulsion systems, which record every tiny detail about their operation.
The changes in data in real-time are reported to engineers who will decide the best
course of action such as scheduling maintenance or dispatching engineering teams
should the problem require it.
• Government: A very interesting use of Big Data is in the field of politics to analyze
patterns and influence election results. Cambridge Analytica Ltd. is one such
organization which completely drives on data to change audience behavior and plays
a major role in the electoral process.
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Big Data Use Cases https://www.oracle.com/big-data/guide/what-is-big-data.html
54. NoSQL Databases
Not based on the relational model
Support distributed database architectures
Provide high scalability, high availability, and fault
tolerance
Support large amounts of sparse data
Geared toward performance rather than transaction
consistency
Store data in key-value stores
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56. NoSQL
Advantages Disadvantages
High scalability, availability, and
fault tolerance are provided
Uses low-cost commodity
hardware
Supports Big Data
4. Key-value model improves
storage efficiency
In terms of data consistency, it
provides an eventually consistent
model
Complex programming is
required
There is no relationship support
There is no transaction integrity
support
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57. NoSQL and Relational Databases Comparison
57
NoSQL databases https://dev.to/lmolivera/everything-you-need-to-know-about-nosql-databases-3o3h
58. Figure 2.5 - A Simple Key-value Representation
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62. The External Model
End users’ view of the data environment
ER diagrams are used to represent the external views
External schema: Specific representation of an
external view
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63. Figure 2.8 - External Models for Tiny
College
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64. The Conceptual Model
Represents a global view of the entire database by the
entire organization
Conceptual schema: Basis for the identification and
high-level description of the main data objects
Has a macro-level view of data environment
Is software and hardware independent
Logical design: Task of creating a conceptual data
model
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65. Figure 2.9 - Conceptual Model for Tiny College
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66. The Internal Model
Representing database as seen by the DBMS
mapping conceptual model to the DBMS
Internal schema: Specific representation of an
internal model
Uses the database constructs supported by the chosen
database
Is software dependent and hardware independent
Logical independence: Changing internal model
without affecting the conceptual model
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68. The Physical Model
Operates at lowest level of abstraction
Describes the way data are saved on storage media
such as disks or tapes
Requires the definition of physical storage and data
access methods
Relational model aimed at logical level
Does not require physical-level details
Physical independence: Changes in physical model
do not affect internal model
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