This document provides an overview of database management systems and concepts. It discusses DBMS architecture, data models including relational and object-oriented models, database design topics like normalization and functional dependencies, and database languages. Emerging database technologies are also mentioned such as XML and web data management. Key concepts covered include the three-schema architecture, data independence, and DBMS interfaces.

1. Databases and Database Management
Systems
(Based on Chapters 1-2)
1

2. • DBMS concepts and architecture
• ER model
• Relational Databases
• Relational Algebra
• Query Languages (SQL)
• Database Design : Normalization and
Functional Dependencies
• Storage and Indexing
• Emerging Trends in Database Technology –
Web Data management, XML, Web
mining,
2

3. 1. Basic Definitions
Database: A collection of related data.
Data: Known facts that can be recorded and have an
implicit meaning.
Mini-world: Some part of the real world about which data
is stored in a database. For example, consider student
names, student grades and transcripts at a university.
3

4. Approximation
• The data stored in a database often tries to capture
some aspect of the real-world in an approximate
manner
• Often not all real-world information is (or can be)
captured
– Generally, only the information, which is
estimated to be needed for decision-making, is
recorded in the database
4

5. Summarization
• Sometimes, data could be stored in a database in a
summarized form
• Example1: In a student database, the “grades”
column provides a summary
– It does not record explicitly what the student scored in
mid-sems, end-sems, labs etc
• Example 2: In a database containing sales info, the
number of units (of a product) sold may be shown as
a single aggregated number
– That is, it may not record explicitly how many units were
sold during each hour, but only records the total sales for
the whole day 5

6. Summarization, materiality and usefulness
• The extent to which you summarize the data largely
depends on the intended use of the database
• Example: If you just want to know a student’s
grades, recording the summarized grade is adequate
– But if you also want to know whether the student is good
in theory or in lab work, you would need to record theory
and lab grades separately
• Materiality criteria: Is recording the data at a certain
level of detail relevant to decision-making (i.e.,
intended use of the data)?
– Too much detail may not be required, and could even be
counter-productive e.g., clouding decision-making 6

7. Semantics of the data
• Semantics of the data: What does the data really
mean?
• Sometimes, by looking at a column name e.g.,
student name, you will know that this column
contains student names
• But assume a column name called “student grades”
– This does not really tell you how the grades were
calculated, and a grade of B corresponds to what absolute
score etc
7

8. Semantics of the data
• Now assume a column called “Total sales” in a sales
database
– This does not really tell you whether the information is about total
sales for 1 day, 1 month or 1 year
– Are the total sales figures about the sales done only within India,
or this is about worldwide sales?
– These total sales figures pertain to which month?
– Does this include/exclude the effect of refunds (returned products
e.g., due to defects)?
– Do these sales figures correspond to some marketing campaign or
sales strategy of the company? (Example: If you offer 50%
discount, you are highly likely to sell more than if you offer 2%
discount)
– What percentage of the sales were done in bulk e.g., to suppliers
instead of individual consumers?
8

9. Semantics of the data
• Bottomline: To be able to query and use the data,
you need to understand clearly what the data means,
the context surrounding the data and the
assumptions associated with the data
• Without understanding the semantics of the data,
you cannot figure out and interpret the meaning of
the query results that the database will provide to
you
9

10. Database Management System (DBMS): A software
package/ system to facilitate the creation and
maintenance of a computerized database.
It
• defines (data types, structures, constraints)
• construct (storing data on some storage medium
controlled by DBMS)
• manipulate (querying, update, report generation)
databases for various applications.
Database System: The DBMS software together with
the data itself. Sometimes, the applications are also
included.
10

11. 2. Example of a Database
(Conceptual Data Model)
Mini-world for the example: Part of a UNIVERSITY environment.
Some mini-world entities (Data elements):
- STUDENTs
- COURSEs
- SECTIONs (of COURSEs)
- (academic) DEPARTMENTs
- INSTRUCTORs
Some mini-world relationships:
- SECTIONs are of specific COURSEs
- STUDENTs take SECTIONs
- COURSEs have prerequisite COURSEs
- INSTRUCTORs teach SECTIONs
- COURSEs are offered by DEPARTMENTs
- STUDENTs major in DEPARTMENTs 11

12. Figure 1.1: A simplified database system environment,
illustrating the concepts and terminology discussed in
Section 1.1
12

13. Figure 1.2: An example of a database that stores student records
and their grades.
13

14. File Processing and DBMS
File Systems :
– Store data over long periods of time
– Store large amount of data
However :
– No guarantee that data is not lost if not backed up
– No support to query languages
– No efficient access to data items unless the location is known
– Application depends on the data definitions (structures)
– Change to data definition will affect the application programs
– Single view of the data
– Separate files for each application
– Limited control to multiple accesses
- Data viewed as physically stored 14

15. 3. Main Characteristics of Database Technology
- Self-contained nature of a database system: A
DBMS catalog stores the description (structure,
type, storage format of each entities) of the
database. The description is called meta-data).
This allows the DBMS software to work with
different databases.
- To understand meta-data, think of a web-page
which contains certain tags
- Example: A webpage containing a UNIX tutorial
could have tags related to some of the UNIX
commands 15

16. Meta-data
- Meta-data is essentially “data about the
data”
- Often meta-data is a set of keywords which try
to approximately describe the data
- Meta-data is often used for facilitating
search
16

17. Main characteristics (Cont.)
• Insulation between programs and data: Called
program-data independence. Allows changing
data storage structures and operations without
having to change the DBMS access programs.
• You will see this concept of “independence”
throughout CS in general
• The whole idea of creating layers is to ensure that
whatever you change in one layer will have no effect
on the other layer
17

18. Main characteristics (Cont.)
• Data Abstraction: A data model is used to hide
storage details and present the users with a
conceptual view of the database; does not include
how data is stored and how the operations are
implemented.
• Now can anyone tell me why data abstraction is
important?
– Answer is on the next slide
18

19. Why is data abstraction important?
• We do not want the user to be burdened with all the
details of how the data is being actually stored,
which data structures are being used etc
• If we did not have any data abstraction, databases
would not be very usable/consumable because we
cannot expect every user to go through this learning
curve of understanding data structures, storage,
indexing etc before they can even start to use a
database
• Users of databases come from diverse backgrounds
i.e., they are not necessarily only CS majors 19

20. • Support of multiple views of the data: Each user
may see a different view of the database, which
describes only the data of interest to that user.
– This could sometimes relate to access control
• Example: The professor is allowed to see all student grades,
but each student can only see his own grade from the database
• Sharing of Data and Multiple users
20

21. Figure 1.3: Internal storage format for a STUDENT
RECORD
21

22. Figure 1.4:Two views derived from the example database
shown in Figure 1.2 (a) The student transcript view. (b)
The course prerequisite view.
22

23. DBA – Database Administrator
- Responsible for authorizing access to the database,
coordinating, monitoring its use, acquiring hardware,
software needed.
Database designers
- Responsible for identifying the data to be stored, storage
structure to represent and store data. This is done by a team
of professionals in consultation with users, and
applications needed.
23

24. Type of data stored: Temporal angle
- The type of data to be stored often changes over time
- For example: The amount of data stored now per
customer interaction is much more than the amount of
data that used to be stored 10 years ago
- Storing more data about a given user allows for more
personalized services (some privacy constraints exist)
- In practice, as more personalization becomes desirable,
you need to store data at a more detailed level
24

25. 4. Additional Benefits of Database Technology
- Controlling redundancy in data storage and in development and
maintenance efforts.
- Sharing of data among multiple users.
- Restricting unauthorized access to data.
- Providing multiple interfaces to different classes of users.
- Representing complex relationships among data.
- Enforcing integrity constraints on the database.
- Providing backup and recovery services.
- Potential for enforcing standards.
- Flexibility to change data structures.
- Reduced application development time.
- Availability of up-to-date information.
• Economies of scale. 25

26. Figure 1.5: The redundant storage of Data items. (a) Controlled
Redundancy: Including StudentName and CourseNumber in the
grade_report file. (b) Uncontrolled redundancy: A
GRADE_REPORT record that is inconsistent with the
STUDENT records in Figure 1.2, because the Name of student
number 17 is Smith, not Brown.
26

27. 5 When not to use a DBMS
Main inhibitors (costs) of using a DBMS:
- High initial investment and possible need for additional hardware.
- Overhead for providing generality, security, recovery, integrity,
and concurrency control.
When a DBMS may be unnecessary:
- If the database and applications are simple, well defined, and not
expected to change.
- If there are stringent real-time requirements that may not be met
because of DBMS overhead.
- If access to data by multiple users is not required.
When no DBMS may suffice:
- If the database system is not able to handle the complexity of data
because of modeling limitations
- If the database users need special operations not supported by the
DBMS.
27

28. 6. Data Models
Data Model: A set of concepts to describe the structure
(data types, relationships) of a database, and certain
constraints that the database should obey.
Data Model Operations: Operations for specifying
database retrievals and updates by referring to the
concepts of the data model.
28

29. Categories of data models:
- Conceptual (high-level, semantic) data models: Provide
concepts that are close to the way many users perceive
data. (Also called entity-based or object-based data
models.)
- Physical (low-level, internal) data models: Provide
concepts that describe details of how data is stored in the
computer.
- Implementation (record-oriented) data models: Provide
concepts that fall between the above two, balancing user
views with some computer storage details.
29

30. 6A. HISTORY OF DATA MODELS
• Relational Model: proposed in 1970 by E.F. Codd (IBM), first
commercial system in 1981-82. Now in several commercial
products (ORACLE, SYBASE, INFORMIX, INGRES).
• Network Model: the first one to be implemented by Honeywell in
1964-65 (IDS System).
Adopted heavily due to the support by CODASYL (CODASYL -
DBTG report of 1971).
Later implemented in a large variety of systems - IDMS (Cullinet -
now CA), DMS 1100 (Unisys), IMAGE (H.P.), VAX -DBMS
(Digital).
• Hierarchical Data Model : implemented in a joint effort by IBM and
North American
Rockwell around 1965. Resulted in the IMS family of systems. The
most popular model.
Other system based on this model: System 2k (SAS inc.) 30
•

31. Object-oriented Data Model(s) : several models have
been proposed for implementing in a database
system. One set comprises models of persistent
O-O Programming Languages such as C++ (e.g.,
in OBJECTSTORE or VERSANT), and Smalltalk
(e.g., in GEMSTONE). Additionally, systems like
O2, ORION (at MCC - then ITASCA), IRIS (at
H.P.- used in Open OODB).
• Object-Relational Models : Most Recent Trend.
Exemplified in ILLUSTRA and UNiSQL systems.
31

32. Figure 2.1: Schema diagram for the database of Figure 1.2
32

33. 7. Schemas versus Instances
Database Schema: The description of a database. Includes
descriptions of the database structure and the constraints that
should hold on the database.
Schema Diagram: A diagrammatic display of (some aspects of) a
database Schema.
Database Instance: The actual data stored in a database at a
particular moment in time . Also called database state (or
occurrence).
The database schema changes very infrequently . The database
state changes every time the database is updated . Schema is also
called intension, whereas state is called extension. 33

34. 8. Three-Schema Architecture
Proposed to support DBMS characteristics of:
- Program-data independence.
- Support of multiple views of the data.
Defines DBMS schemas at three levels :
- Internal schema at the internal level to describe data
storage structures and access paths. Typically uses a physical
data model.
- Conceptual schema at the conceptual level to describe the
structure and constraints for the whole database. Uses a
conceptual or an implementation data model.
-
34

35. External schemas at the external level to describe the various
user views. Usually uses the same data model as the
conceptual level.
Mappings among schema levels are also needed. Programs
refer to an external schema, and are mapped by the DBMS
to the internal schema for execution.
35

36. Figure 2.2: Illustrating the three-schema architecture
36

37. 9 Data Independence
Logical Data Independence: The capacity to change the conceptual
schema without having to change the external schemas and their
application programs.
Physical Data Independence: The capacity to change the internal
schema without having to change the conceptual schema.
When a schema at a lower level is changed, only the mappings
between this schema and higher-level schemas need to be changed
in a DBMS that fully supports data independence. The higher-
level schemas themselves are unchanged.
Hence, the application programs need not be changed since they refer
to the external schemas.
37

38. 10. DBMS Languages
Data Definition Language (DDL): Used by the DBA and database
designers to specify the conceptual schema of a database.
In many DBMSs, the DDL is also used to define internal and external
schemas (views). In some DBMSs, separate storage definition
language (SDL) and view definition language (VDL) are used to
define internal and external schemas.
Data Manipulation Language (DML): Used to specify database
retrievals and updates.
- DML commands (data sublanguage) can be embedded in a
general-purpose programming language (host language), such as
COBOL, PL/1 or PASCAL.
- Alternatively, stand-alone DML commands can be applied
directly (query language).
38

39. High Level or non-Procedural DML – Describes
what data to be retrieved rather than how to
retrieve.
- Process many records at a time
- SQL
Low Level or Procedural DML – It needs
constructs for both, what to retrieve and what to
retrieve
- Uses looping etc. like programming languages
Only access one record at a time
39

40. 11. DBMS Interfaces
-Stand-alone query language interfaces.
- Programmer interfaces for embedding DML in programming
languages:
- Pre-compiler Approach
- Procedure (Subroutine) Call Approach
- User-friendly interfaces:
- Menu-based
- Graphics-based (Point and Click, Drag and Drop etc.)
- Forms-based
- Natural language
- Combinations of the above
- Speech as Input (?) and Output
- Web Browser as an interface
-
40

41. Parametric interfaces using function keys.
- Report generation languages.
- Interfaces for the DBA:
- Creating accounts, granting authorizations
- Setting system parameters
- Changing schemas or access path
41

42. Figure 2.3: Typical component modules of a DBMS. Dotted lines
show accesses that are under the control of the stored data manager.
42

43. 13. Database System Utilities
To perform certain functions such as:
- Loading data stored in files into a database.
- Backing up the database periodically on tape.
- Reorganizing database file structures.
- Report generation utilities.
- Performance monitoring utilities.
- Other functions, such as sorting , user monitoring , data
compression , etc.
Data dictionary / repository:
- Used to store schema descriptions and other information such
as design decisions, application program descriptions, user
information, usage standards, etc.
- Active data dictionary is accessed by DBMS software and
users/DBA.
43
- Passive data dictionary is accessed by users/DBA only.

44. 14. Classification of DBMSs
Based on the data model used:
- Traditional: Relational, Network, Hierarchical.
- Emerging: Object-oriented, Object-relational.
Other classifications:
- Single-user (typically used with micro- computers) vs.
multi-user (most DBMSs).
- Centralized (uses a single computer with one database) vs.
distributed (uses multiple computers, multiple databases)
Distributed Database Systems have now come to be known as
client server based database systems because they do not
support a totally distributed environment, but rather a set of
database servers supporting a set of clients. 44

45. Weekly assignment
• Go through the lecture slides thoroughly
– Your TA will send you a link to inform you where to
download the lecture slides
• Reading assignment:
– Read Chapters 1-2 of your textbook
• If you have any doubts/questions, pls let me know
45

46. Weekly assignment
• Remember that in this course, you will often need to
understand the concepts of Chapter N before you
can deal with Chapter N+1
• Do not fall behind the class
46

47. Weekly assignment (Cont.)
– Refer to the following:
http://www2.sims.berkeley.edu/research/projects/how-much-info-
2003/
• This website provides information about how much data exists in the world,
and will broaden your perspective on data management in general
• Reading through the entire website is not required for this course, but I want
all of you to read through the “Summary of Findings”
http://www2.sims.berkeley.edu/research/projects/how-much-info-
2003/execsum.htm
• While reading, pay particular attention to Section 5, where the authors
discuss issues concerning assumptions and estimates
• Questions about the exact numbers will not be asked in your
exams, but you should be prepared to answer questions about
the general material in the “Summary of Findings” e.g., 47
assumptions, estimates etc

48. Weekly assignment
• After reading through the “Summary of Findings”,
prepare a 1-2 page report to indicate what you
learned by reading through the material
• Submit the report to your course TA in hard-copy
format latest by Jan 22, 5 pm IST
– No submissions will be accepted after the deadline
48

49. Weekly assignment
• You can choose to format the report in any way you
like
– Focus on the content of the report, not on minor details
such as font size, what font to use etc
– The report will be graded based on content, hence
mention only the key points
– You will get full grade in this assignment as long as you
mention some key points that you learned
– Printouts are preferable, but handwritten reports are also
ok as long as your handwriting is understandable
49