DBMS

Data Structure vs. File Structure
2

Data Structure : How to arrange data in

memory
File Structure : How to arrange data in Disk
and/or any other secondary storage
DataBase and DataBase Management System
Users do NOT have to care about how to store data in a
file. DBMS will handle the detail.
 Users can use SQL (Structured Query Language) to
access the DataBase in an interactive command and/or
through a program (embeded SQL)


RAM Disk

File Types
3

Sequential file: one accessed in a serial manner from

beginning to end. E.g. audio, video, text, programs.
Text file: sequential file in which each logical record
is a single character.
ASCII: 1 byte/char
Unicode: 2 bytes/char

Sequential Files
4

Sequential file: A file whose contents can only be

read in order



Reader must be able to detect end-of-file (EOF)
Data can be stored in logical records, sorted by a key field


Greatly increases the speed of batch updates

Text Files
5

Simple file structure.
Extendable to more complex file structures using

markup languages (XHTML, HTML).
XHTML, HTML control the display of the file on a
monitor.
XML is a standard for markup languages.

Converting data from two’s
complement notation into ASCII for
6
storage in a text file

A procedure for merging two sequential files
7

Applying the merge
algorithm (Letters
are used to
represent entire
records.
The particular letter
indicates the value
of the record’s
key field.)

8

XML
9

Example

<note>
<to>Tove</to>
<from>Jani</from>
<heading>Reminder</heading>
<body>Don't forget me this weekend!</body>
</note>
http://www.w3schools.com/xml/xml_whatis.asp

Indexed File
key

pointer

Brown

P1

Johnson

P3

Smith

P4

Watson

P5

Data item
for Brown

P2

Jones

10

Index file

Data file

Index: A list of key values and the location
of their associated records

A file with a partial index
12

Hashing
14

In hashing the index file is replaced by a hash

function.

The storage space is divided into buckets.
 Each record has a key field. Each record is stored in the
bucket corresponding to the hash of its key.
 A hash function computes a bucket number for each
key value.
Advantage: no index table needed.
Disadvantages:


i) hash function needs careful
design;
ii) unpredictable performance

Terminology
15

Bucket: section of the data storage area.
Key: identifier for a block of information.
Hash function: takes as input a key and outputs a

bucket number.
Collision: two keys yield the same bucket number.

Hash Functions
16

Hash Function Requirements
 Easily and quickly computed.
 Values evenly spread over the bucket numbers.
 What can go wrong: bucket number computed from 1st
and 3rd characters of a name:
Brown, Brook, Broom, Broadhead,
Biot, Bloom,
…
 Examples of Hash Functions
 Mid square: compute (key x key) and
set bucket number = middle digits.
 Extraction: select digits from certain positions within
the key.
 Divide key by number of buckets and use the
remainder.

Hashing the key field value 25X3Z to one of
41 buckets
17

The rudiments of a hashing system, in which each
bucket holds those records that hash to that
18
bucket number

Collisions in Hashing
19

Collision: The case of two keys hashing to the

same bucket
Clustering problem: Poorly designed hashing function
can have uneven distribution of keys into buckets
 Collision also becomes a problem when there aren’t
enough buckets (probability greatly increases as load
factor (% of buckets filled) approaches 75%)
 Solution: somewhere between 50% and 75% load factor,
increase number of buckets and rehash all data


A large file partitioned into buckets
to be accessed by hashing
21

The role of an operating system
when accessing a 22
file

System calls ?

Maintaining a file’s order by means of
a File Allocation Table (FAT)
23

Information Required on a Hard Drive to Load
24

Startup BIOS (POST , Load MBR)
Master Boot Record (MBR)
 Master Boot Program
 Partition Table (16 bytes * 4)
OS Boot Record (Boot Sector)
 Loads the first program file of the OS
Boot Loader Program
 Begins process of loading OS into memory

an OS

How Data Is Logically Stored on a Floppy Disk
25

All floppy and hard disk drives are divided into

tracks and sectors

Tracks are concentric circles on a disk
Sector

Always 512 bytes
 Physical organization of a disk
 BIOS manages disk as sectors
Cluster (file allocation unit)


Group of sectors
 Logical organization of a disk
 OS views disk as a list of clusters


The Boot Record
26

Track 0, sector 1 of a floppy disk
Contains basic information about how the disk is

organized
Includes bootstrap program, which can be
used to boot from the disk
Uniform layout and content of boot record allows
any version of DOS or Windows to read any DOS
or Windows disk

The File Allocation Table (FAT)
27

Lists the location of files on disk in a one-column

table
Floppy disk FAT is 12 bits wide, called FAT12
Each entry describes how a cluster on the disk is
used
A bad cluster on the disk will be marked in the
FAT

The Root Directory
28

Lists all the files assigned to this table
Contains a fixed number of entries
Some items included are:


Filename and extension



Time and date of creation or last update



File attributes



First cluster number

How a Hard Drive is Logically Organized to Hold
Low-level format
Data
29


Creates tracks and sectors, done at factory

Partition the hard drive (FDISK.EXE)


Creates partition table at the beginning of drive

High-level format


Done by OS for each logical drive

Master Boot Record (MBR) is the first 512 bytes

of a hard drive

Master boot program (446 bytes) calls boot program to
load OS
 Partition table




Description, Location, Size

Partitions and Logical Drives
30

FAT16
31

Supported by DOS and all versions of Windows
Uses 16 bits for each cluster entry
As the size of the logical drive increases, FAT16

cluster size increases dramatically

FAT32
32

Became available with Windows 95 OSR2
Used 32 bits per FAT entry, although only 28 bits

were used to hold cluster numbers
More efficient than FAT16 in terms of cluster size

NTFS
33

Supported by Windows NT/2000/XP
Provides greater security
Used a database called the master file table

(MFT) to locate files and directories

Supports large hard drives

Comparing FAT16, FAT32, NTFS
34

A file versus a database organization
35

(1/2)

A file versus a database organization
(2/2)
36

The conceptual layers of a database
37

Schemas
38

Schema: A description of the structure of an entire

database, used by database software to maintain the
database
Subschema: A description of only that portion of
the database pertinent to a particular user’s needs,
used to prevent sensitive data from being accessed
by unauthorized personnel

Database Management Systems
39

Database Management System (DBMS): A

software layer that manipulates a database in
response to requests from applications
Distributed Database: A database stored on
multiple machines


DBMS will mask this organizational detail from its users

Data independence: The ability to change the

organization of a database without changing the
application software that uses it

Database Models
40

Database model: A conceptual view of a database



Relational database model
Object-oriented database model

Relational Database Model
41

Relation: A rectangular table



Attribute: A column in the table
Tuple: A row in the table

Relational Design


Avoid multiple concepts within one relation



Can lead to redundant data
Deleting a tuple could also delete necessary but unrelated
information

Improving a Relational Design
42

Decomposition: Dividing the columns of a

relation into two or more relations, duplicating those
columns necessary to maintain relationships


Lossless or nonloss decomposition: A “correct”
decomposition that does not lose any information

A relation containing employee information
43

A relation containing redundancy
44

An employee database consisting of
three relations
45

Finding the departments in which employee
has worked
46

A relation and a proposed decomposition
47

Relational Operations
48

Select: Choose rows
Project: Choose columns
Join: Assemble information from two or more

relations

Another example of the JOIN
operation
52

An application of the JOIN operation
53

The associations between objects in an objectoriented database
59

Maintaining Database Integrity (1/2)
60

Transaction: A sequence of operations that must

all happen together


Example: transferring money between bank accounts

Transaction log: A non-volatile record of each

transaction’s activities, built before the transaction
is allowed to execute
Commit point: The point at which a transaction has
been recorded in the log
 Roll-back: The process of undoing a transaction


Maintaining database integrity
(2/2)
61

Simultaneous access problems
 Incorrect summary problem
 Lost update problem
Locking = preventing others from accessing data

being used by a transaction



Shared lock: used when reading data
Exclusive lock: used when altering data

Data Mining
62

Data Mining: The area of computer science that

deals with discovering patterns in collections of data
Data warehouse: A static data collection to be
mined


Data cube: Data presented from many perspectives to
enable mining

Data Mining Strategies
 Class description
 Class discrimination
 Cluster analysis
 Association analysis
 Outlier analysis
 Sequential pattern analysis

Social Impact of Database Technology
63

Problems
 Massive amounts of personal data are being collected





Often without knowledge or meaningful consent of affected
people

Data merging produces new, more invasive information
Errors are widely disseminated and hard to correct

Remedies
 Existing legal remedies often difficult to apply
 Negative publicity may be more effective

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