Lecture 01 - Data, Information, knowledge, and Data Models.pptx

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© 2023 Melbourne Institute of Technology. All rights reserved
> www.mit.edu.au
MN405 Data and Information Management
Data, Information, knowledge, and Data Models
Lecture-1

• The content of this lecture slide is
adopted from the following book:
• C. Coronel, S. Morris. Database
Systems: Design, Implementation, &
Management, 14th ed, Cengage
Learning, 2023. Ch-1&2.
Acknowledgment

© 2023 Melbourne Institute of Technology. All rights reserved Compiled By: Dr Md Waliur Rahman Miah
•Define the difference between data and information
•Describe what a database is, and how it is important for decision making
•Explain the importance of database design
•Understand flaws in file system data management
•Outline the main components of the database system
•Describe the main functions of a database management system (DBMS)
•Discuss data modeling and the basic data-modeling building blocks
•Define what business rules are and how they influence database design
•List emerging alternative data models and the needs they fulfill
Lecture-1 Objectives
By the end of this lecture, you should be able to:
3

© 2023 Melbourne Institute of Technology. All rights reserved Compiled By: Dr Md Waliur Rahman Miah 4
Why Databases?
4

•Data — raw facts
•Information — result of processing raw data
— reveal context/ meaning of data
•Knowledge implies familiarity, awareness, and
understanding of information
•Accurate, relevant, and timely information is
the key to good decision making
•Data management — discipline — proper
generation, storage, and retrieval of data
Data versus Information (1 of 2)
5

Data versus Information (2 of 2)
Figure 1.2 Transforming Raw Data into Information
Compiled By: Dr Md Waliur Rahman Miah
6

Introducing the Database
• Database :
−shared, integrated computer structure
−stores a collection of:
−user data and
−Metadata
•Database management system (DBMS) :
−collection of programs
−manages the database structure
−controls access to the data stored in the database
7

Role and Advantages of DBMS (1 of 2)
•A DBMS provides the following advantages:
−Presents a single, integrated view of the data
−Improved data sharing
−Improved data security
−Better data integration
−Minimized data inconsistency
−Improved data access
−Improved decision making
−Increased end-user productivity
8

Role and Advantages of DBMS (2 of 2)
Figure 1.4 The DBMS Manages the Interaction Between the End User and the Database
9

10
Types of Databases (1 of 3)
•Classification by users:
•Single-user database —
−stand-alone database on a personal computer
•Multiuser database
− workgroup database
− enterprise database
•Classification by location
− centralized database
− distributed database
− cloud database

•Classification by data type
−General-purpose databases
−Discipline-specific databases
−operational database
−analytical database
−two main components:
 data warehouse
 online analytical processing (OLAP)
11

•Classification by Data-structure:
−Unstructured data
−Structured data
−Semistructured
−XML database
•You may also find:
•Social media database
•NoSQL database
12

Why Database Design is Important (1 of 3)
•Database design
— involves planning database structure to store and manage data
— Data must be properly decomposed into its constituent parts before storing.
•A well-designed database:
— facilitates data management
— generates accurate and valuable information
•A poorly designed database:
— causes difficult-to-trace errors
— lead to poor decision making
13

Figure 1.5 Employee Skills Certification in a Poor Design
14

Figure 1.6 Employee Skills
Certification in
a Good Design
15

Evolution of File System Data Processing (1 of 2)
•Manual file systems:
—file folders and
—filing cabinets
•Computerized file systems:
—a data processing (DP) specialist created a computer-based filing system
—track data and produce required reports
•File system redux in modern end-user productivity tools
−Business users widely use spreadsheet programs such as Microsoft Excel to enter
data in a series of rows and columns to manipulate data
−A common misuse of spreadsheets is as a substitute for a database
−Spreadsheets “file system” suffers from the same problems as the early file systems
16

Evolution of File System Data Processing (2 of 2)
Table 1.2 Basic File Terminology
Term Definition
Data Raw facts, such as a telephone number, a birth date, a customer name, and a year-to-date
(YTD) sales value. Data has little meaning unless it has been organized in some logical
manner.
Field A character or group of characters (alphabetic or numeric) that has a specific meaning. A
field is used to define and store date.
Record A logically connected set of one or more fields that describes a person, place, or thing. For
example the fields that constitute a record for a customer might consist of the customer’s
name, address, phone number, date of birth, credit limit, and unpaid balance.
File A collection of related records. For example, a file might contain data about the students
currently enrolled at Gigantic University.
17

Problems with File System Data Processing
−Lengthy development times
−Difficulty of getting quick answers
−Complex system administration
−Lack of security and limited data sharing
−Extensive programming
18

Structural and Data Dependence
•A file system exhibits:
—Structural dependence
—Data dependence
•Structural and data dependence makes the file system cumbersome
19

Data Redundancy
•Data redundancy — same data is stored unnecessarily at different places
− islands of information for scattered data locations
− may result different versions of the same data
•Possible results of uncontrolled data redundancy
−Poor data security
−Data inconsistency
−Data-entry errors
−Data integrity problems
20

Data Anomalies
•A data anomaly
— not all of the required changes made successfully
•three types of anomalies:
−Update anomalies
−Insertion anomalies
−Deletion anomalies
21

Database Systems (1 of 2)
•Database system :
−logically related data stored in a single logical data repository
−the data might be physically distributed among multiple
storage facilities
•DBMS eliminates most of the file system’s problems:
−data inconsistency, data anomaly, data dependence, and
structural dependence
•DBMS software :
−stores data structures, relationships between those structures,
and access paths to those structures
−defines, stores, and manages all access paths and components
22

Database Systems (2 of 2)
Figure 1.9 Contrasting Database
and File Systems
23

The Database System Environment (1 of 2)
•Database system -- define and regulate the collection, storage,
management, and use of data
•The database system is composed of the following five components:
−Hardware
−Software
−People
−Procedures
−Data
•Database solutions must be cost-effective as well as tactically and
strategically effective 24

The Database System Environment (2 of 2)
25
Figure 1.10 The Database
System Environment

DBMS Functions (1 of 3)
•A DBMS performs the following important functions:
−Data dictionary management — definitions of data elements and
relationships
−Data storage management — structures with performance tuning
−Data transformation and presentation — conform to required data structures
and logical expectations
−Security management — enforces user security and data privacy
26

•A DBMS performs the following important functions (continued):
−Multiuser access control – without compromising integrity
−Backup and recovery management – ensure data safety and integrity
−Data integrity management – enforces integrity rules, thus minimizing
redundancy and maximizing data consistency
27

•A DBMS performs the following important functions (continued):
−Database access languages and application programming interfaces
– eg SQL to access data, and API for Python, Java, and C to
use the database.
−Database communication interfaces
– e.g JDBC, mysql.connector etc
28

Disadvantages of Database System
−Increased costs
−Management complexity
−Maintaining currency (updated to latest system))
−Vendor dependence
−Frequent upgrade/replacement cycles
29

Data Modeling

•Data modeling
— Process of creating a specific data model for a problem domain
— Data modeling is an iterative, progressive process
•Data model is a relatively
— simple representation of complex real-world data structures
•Database designers:
— use data-modeling constructs and powerful database design tools
— reduce errors in database modeling
Data Modeling and Data Models
31

• Entity
• Attribute
• Relationship
• Constraints
•The following are three different types of relationships:
— One-to-many (1:M or 1..*) relationship
— Many-to-many (M:N or *..*) relationship
— One-to-one (1:1 or 1..1) relationship
Data Model Basic Building Blocks (1 of 2)
32

Constraint — restriction on data
— ensure data integrity
— expressed in the form of rules eg.:
— an employee’s salary must have values that are
between 6,000 and 350,000
— a student’s GPA must be between 0.00 and 4.00
— each class must have one and only one teacher
Data Model Basic Building Blocks (2 of 2)
33

•Business rule
— precise and unambiguous description of
— policy, procedure, or principle
— within an organization
•Used to define entities, attributes, relationships, and constraints
•Examples of business rules :
— A customer may generate many invoices
— An invoice is generated by only one customer
•Main sources of business rules:
— company managers, policy makers, department managers, and written
documentation
Business Rules
34

“A customer may generate many invoices.”
•From this business rule, deduce the following:
— Customer and invoice are objects of interest for the business and
should be represented by their respective entities
— There is a “generate” relationship between customer and invoice
•The rule above is complemented by another business rule:
“An invoice is generated by only one customer.”
— The relationship then is one-to-many (1:M)
Translating Business Rules into
Data Model Components
35

•Entity names — descriptive of the objects
•Attribute name — descriptive of the data represented
• prefix the name of an attribute with the name or abbreviation of the
entity in which it occurs
— For example, in the CUSTOMER entity, customer’s credit limit may be
called CUS_CREDIT_LIMIT
•A proper naming convention can help make your model self-documenting
Naming Conventions
36

•Hierarchical and Network Models
•Relational Model
•Entity Relationship Model
•Object-Oriented Model
•Object/Relational and XML
•Emerging Data Models: Big Data and NoSQL
Different Data Models
37

•Hierarchical model
—developed in the 1960s
—manage large amounts of data for complex manufacturing projects
•Hierarchical structure contains levels, or segments
•Segment — equivalent of a file system’s record type
— higher layer — parent segment
— directly beneath it is called the child
•The network model was created to represent complex data relationships more
effectively than the hierarchical model
Hierarchical and Network Models (1 of 2)
38

•Concepts emerged with the network model — still used by
modern data models:
—Schema
—Subschema
—Data manipulation language (DML)
—Data definition language (DDL)
Hierarchical and Network Models (2 of 2)
39

•Foundation of this model is a mathematical concept known as a relation
— relation — two-dimensional structure composed of rows and columns
— row represents tuple/record, and column represents an attribute
•Implemented through sophisticated relational database management system
(RDBMS)
•RDBMS manages all the underlying details,
— while the users sees a collection of tables in which the data is stored
Relational Model
40

•Database designers prefer to use simple graphical tools
— to visualize entities and their relationships
— simplify representation of complex database design
— The entity relationship (ER) model (ERM) was developed to do this
•The relational data model and ERM combined to provide the foundation for complex
tightly structured database design
•An entity relationship diagram (ERD) uses graphical representations to model
database components
Entity Relationship Model (1 of 3)
41

•ER model components:
— Entity – rectangle box
— Attributes – texts in the box
— Relationships – association-lines among data (or diamond box)
• three ER notations:
— Chen notation
— Crow’s Foot notation
— Class diagram notation (part of the Unified Modeling Language (UML))
42

Figure 2.3 The ER Model Different Notations
43

•Object-oriented data model (OODM) — both data and its relationship are contained
in a single structure known as an object
— OODM — basis for OODBMS (object-oriented database management system)
— OODM — semantic data model — indicates meaning
•OODM components: [continued next slide]
— Object — abstraction of a real-world entity
— Attributes — properties of an object
— Class
Object-Oriented Model (1 of 3)
44

•OODM components (continued):
— Class — collection of objects with similar structure and behavior
— Method — real-world action such as finding a selected PERSON’s name,
changing a PERSON’s name, or printing a PERSON’s address
— Class hierarchy —upside-down tree where each class has only one parent
— Inheritance — ability of an object to inherit the attributes and methods of
parent classes
•Object-oriented data models are typically depicted using Unified Modeling Language
(UML) class diagrams
45

Figure 2.4 A Comparison of the OO, UML, and ER Models
46

•Extended relational data model (ERDM)
— adds OO model’s features within the relational database structure
— ERDM DBMS is often called object/relational database
management system (O/R DBMS)
•O/R DBMSs advantages:
— model’s conceptual simplicity
— data integrity
— easy-to-use query language
— high transaction performance
— high availability, security, scalability, and expandability
•Applications example: OLTP and OLAP database
Object/Relational DBMS
47

•Internet of Things (IoT) has accelerated the rate of data growth
— about 2.5 quintillion bytes of data are created daily
•Big Data — movement to find:
— new and better ways to manage large amounts of data
— web- and sensor-generated data
— derive business insight from it
•Basic characteristic of Big Data databases:
— volume, velocity, and variety, or the 3 Vs
Emerging Data Models: Big Data and NoSQL (1 of 3)
48

Example of Big Data technologies:
— Hadoop — Java-based, open-source, high-speed, fault-tolerant
distributed storage and computational framework
— Hadoop Distributed File System (HDFS)
— MapReduce
— NoSQL — large-scale distributed database system that stores
structured and unstructured data in efficient ways
49

•NoSQL databases general characteristics:
— not based on the relational model and SQL
— support highly distributed database architectures
— provide high scalability, high availability, and fault tolerance
— support very large amounts of sparse data
— geared toward performance rather than transaction consistency
50

Figure 2.5 The Evolution
of Data Models
Data Models:
Summary
51

Summary (1 of 2)
•Define the difference between data and information
•Describe what a database is, the various types of databases, and why they
are valuable assets for decision making
•Explain the importance of database design
•Understand flaws in file system data management
•Outline the main components of the database system
•Describe the main functions of a database management system (DBMS)
52

Summary (2 of 2)
•Discuss data modeling and why data models are important
•Describe the basic data-modeling building blocks
•Define what business rules are and how they influence database design
•Understand how the major data models evolved
•List emerging alternative data models and the needs they fulfill
53

> www.mit.edu.au
Dr Md Waliur Rahman Miah
wmiah@academic.mit.edu.au
MIT Melbourne
288 La Trobe Street,
Melbourne, VIC 3000,
Australia
Phone:+61 03 8600 6700

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