DBMS | fundamentals of DBMS | Data | Information | Knowledge | Data Base | Feature of data in database | Operations performed on the database |Components of database | Advantages of DBMS | Disadvantages of DBMS | Schema | Sub schema | Instances. |DBMS Architecture

DBMS

Data

Data is a valuable asset for an organization. Data is generally thought to be numbers and text. In addition data also includes multimedia files such as an image, audio files, video clips etc.

Information

Information is the manipulated and processed form of data.

Knowledge

Information organized and evaluated in the human's mind so that it can be used purposefully is known as Knowledge.

Data Base

Data is a very valuable resource in the operation and management of an organization. Database is a well organized collection of data that are related in a meaningful way, which can be shared by multiple users but stored only once.

Feature of data in database

• It should be well organized.
• It should be related
• It should be flexible to change.
• It should be recoverable in case of damage
• It should be stored permanently
• It should be shared among different users as well as applications.

Operations performed on the database

• Insertion
• Updation
• Deletion
• Selection

Components of database system environment

• Data
• Hardware
• Software
• Users

Data : the data act as bridge between the machine parts and the users which directly access it.

Data may be of different types

Userdata

Metadata

Application metadata

Hardware : The hardware consists of the secondary storage devices such as magnetic disks, optical disks , magnetic tapes etc.

Software: The software part mainly consist of DBMS which acts as a bridge between user and the database

Users: users are those persons who need information from the database to carry out their primary business responsibilities.

Types of users

Database Administrator

Database Designers

End users

Application programmers

DBMS stand for Database Management System. DBMS is basically a collection of programs that enable user to store, modify, extract information from Database as per the requirements.

Advantages of DBMS

• Controlling data redundancy
• Elimination of inconsistency
• Better services to the users
• Flexibility of the system is improved
• Standards can be enforced
• Security can be improved
• Provide backup and recovery

Disadvantages of DBMS

• Increased complexity
• Confidentiality, privacy and security risk
• Threat to data quality and data integrity
• Enterprise vulnerability
• Complexity of backup and recovery

Schema

The overall design of the database is called schema. A schema shows an overall structure into which the values of all data items are fitted.

Sub schema

A subset of schema is called a sub schema. It inherits the same properties as that of a schema

Instances.

The collection of information stored in the database at a particular moment called instance.

DBMS Architecture

The DBMS architecture is a framework where the structure of the DBMS is described. ANSI-SPARC (American Nationals Standards Institute- Standards Planning and Requirements committee). THE ANSI-SPARC three level architecture consists of the following three levels.

External level

Conceptual level

Internal level

External level : external level also known as individual user view is the highest level of three level DBMS architecture. This level describe the user's view of the database.in this level, only those portions of the database are described that are relevant to the user or applications program and hides the rest of the database details.

Conceptual level : the conceptual level  sometimes known as logical level that describe the logical structure of the whole database for user i.e. global view of data. it is represented as the middle level in the three level architecture. The conceptual view is defined by the conceptual schema which describe all the database entities, attributes, and relationships together with constraints.

Internal level : the internal level is the lowest level of the three level architecture of DBMS. This level describe how the data will be stored and also describe the data structure and access method to be used by the database.

Mapping between views

The three levels of DBMS architecture don't exist independently of each other. There must be correspondence between the three levels. This correspondence between different levels is known as Mapping.

Types of Mapping

• Conceptual/internal Mapping
• External/Conceptual Mapping

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Graphics |Multimedia | its elements | components | features | advantages | disadvantages

Multimedia

Multimedia Information systems make use of many different ways of communication. These can include text, graphics, image, voice, and video. The term multimedia is generally used to describe more sophisticated systems that support moving images and audio.

In order to work with multimedia a personal computer typically requires a powerful microprocessor, large memory and storage capacity, a high quality monitor, external loudspeakers or headphones and a sound card for improved sound generation, and CD-ROM OR DVD-ROM drive, as well as special software to utilize many of these devices.

  

Interactive multimedia

It means to interface with these media typically with a computer keyboard, mouse, touch screen, on screen buttons, and text entry allowing a user to make decision as to what takes place next with this multimedia

Use of multimedia in education

• Used as reinforcement
• Used to clarify a concept
• Creates the positive attitude of individuals
• The content of topic can be carefully selected
• The length of time needed for instruction can be reduced

Elements of multimedia

• Audio
• Videos
• Graphics
• Animation\
• Texts 

Audio

Audio signals are continuous analog signals. They are first captured by a microphone and then digitized and store usually compressed as CD quality audio requires 16- bit sampling at 44.1 KHz

Video

• Video is a technology of electronically capturing , recording, processing, storing, transmitting a sequence of images representing scenes in motion.
• Video is stored as a standard computer files
• Digital video clearly needs to be compressed
• Analog video is usually captured by a video cameras and then digitized

Graphics

Graphics are the backbone of any multimedia products. Graphics are created using variety of tools including paint brush, AutoCAD, drawing software and digital camera

Graphics are usually conducted by the composition of primitives objects such as lines, polygons, circles, and arcs. Graphics are the visual presentation on some surface. graphics are used to provide the back ground or information content for multimedia product

Animation

Animation is the rapid display of a sequence of images of 2-D or 3-D artwork or model positions in order to create an illusion of movement.

2-D Animation 2-D figures are created and/ or edited on the computer using 2-D bitmap graphics or created and edited using 2D vector graphics. This include automated computerized versions of traditional animation techniques, morphing, onion skinning

3-D animation 3-D animation digital models manipulated by an animator. In order to manipulated a mesh, it is given a digital armature. This process is called rigging.

Text

Text plays an important part in almost all multimedia. The design and content of multimedia texts are so different from other type of texts such as newspaper/ magazine text and book texts.

Designing text

Designing multimedia text involvs controlling two very important characteristics of multimedia text

• Display
• Content

Display deals with "How" parameters of multimedia texts- like "how the text is going to be represented at a given place" which font is going to be used to represent this text and with what color etc.

Content design poor content fail to impress the user and result in loss of interest in the whole project. So the content of the project must be impressive.

Display design text display design in multimedia projects involves decisions in controlling two main display parameters involved with multimedia texts :font and colours

Components of multimedia systems

1. Capture devices
2. Storage devices
3. Communication networks
4. Computer systems
5. Display devices

Features of multimedia systems

1. Very high processing power
2. Multimedia capable file systems
3. Data representation
4. Efficient and high i/o
5. Special operating systems
6. Storage and memory
7. Network support
8. Software tools

Advantages of multimedia

1. Enhancement of text only messages
2. Improve over traditional audio-video presentation
3. Gains and holds attention
4. Good for "computer phobic"
5. Multimedia is entertaining as well as educational

Disadvantage of multimedia

1. Investment cost
2. Technical barriers
3. Legal problems

Soft Computing | Definition of Fuzzy or Fuzzy Logic | Crisp sets | Fuzzy Sets | Fuzzy Relations | Fuzzy Inference System | Fuzzy Expert System

Definition of fuzzy

Fuzzy – “not clear, distinct, or precise; blurred”

Definition of fuzzy logic

A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts.

Introduction to fuzzy logic

Uncertainty is inherent in accessing information from large amount of data; for example words like near and slow in sentences like

“My house is near to the office”

“He drives slowly”

If we set slow as speeds <=20 and fast otherwise, then is 20.1 is fast?

Crisp sets

Crisp sets: In a crisp set, members belong to the group identified by the set or not

  slow = {s such that  0 <= s <= 40}

   fast = {s such that  40 < s <70}

 40.1 belongs to set fast, hence 40.1 is not slow

  Drawback of crisp sets: Suppose a physical system has to apply brakes if the speed of the vehicle is fast and release the brake if the speed is slow. If the speed is in the interval [39, 41], such a system would continuously keep jerking which is not desired

The crisp set is defined in such a way as to divide the individuals in some given universe of discourse into two groups: members and nonmembers.

However, many classification concepts do not exhibit this characteristic.

For example, the set of tall people, expensive cars, or sunny days.

Fuzzy sets

A fuzzy set can be defined mathematically by assigning to each possible individual in the universe of discourse a value representing its grade of membership in the fuzzy set.

For example: a fuzzy set representing our concept of sunny might assign a degree of membership of 1 to a cloud cover of 0%, 0.8 to a cloud cover of 20%, 0.4 to a cloud cover of 30%, and 0 to a cloud cover of 75%.

For example let us evaluate few dates 12, 13, 14, 15, 16 August 2014

Crisp set { (12,1), (13, 1), (14, 0), (15, 1), (16,0)}

Here 12, 13, 15 belongs to sunny set.

Fuzzy set {(12, 0.9), (13, 1), (14, 0.8), (15,1), (16,0.3)}

Here all belongs to sunny set but with definite grade of membership.

A membership function

A characteristic function: the values assigned to the elements of the universal set fall within a specified range and indicate the membership grade of these elements in the set.

Larger values denote higher degrees of set membership.

A set defined by membership functions is a fuzzy set.

The most commonly used range of values of membership functions is the unit interval [0,1].

Fuzzy Sets

To reduce the complexity of comprehension, vagueness is introduced  in crisp sets

Fuzzy set contains elements; each element signifies the degree or grade of membership to a fuzzy aspect

Membership values denote the sense of belonging of a member of a crisp set to a fuzzy set

Example of a fuzzy set

Consider a crisp set A with elements representing ages of a set of people in years

A = { 2, 4, 10, 15, 20, 30, 35, 40, 45, 60, 70}

Classify the age in terms of six fuzzy variables or names given to fuzzy sets as: infant, child, adolescent, adult, young and old

Membership is different from probabilities

Memberships do not necessarily add up to one

Fuzzy Terminology

Universe of Discourse (U): The range of all possible values that comprise the input to the fuzzy system

Fuzzy set: A set that has members with membership (real) values in the interval [0,1]

Membership function: It is the basis of a fuzzy set. The membership function of the fuzzy set A is given by µA: Uà [0,1]

Fuzzy Relations

Generalizes classical relation into one that allows partial membership

Describes a relationship that holds between two or more objects

Example: a fuzzy relation “Friend” describe the degree of friendship between two person (in contrast to either being friend or not being friend in classical relation!)

A fuzzy relation        is a mapping from the Cartesian space X x Y to the interval [0,1], where the strength of the mapping is expressed by the membership function of the relation m    (x,y)

The “strength” of the relation between ordered pairs of the two universes is measured with a membership function expressing various “degree” of strength [0,1]

Fuzzy If-Then Rules

General format:

If x is A then y is B

Examples:

If pressure is high, then volume is small.

If the road is slippery, then driving is dangerous.

If a tomato is red, then it is ripe.

If the speed is high, then apply the brake a little.

LINGUISTIC VARIABLES

A linguistic variable is a fuzzy variable.

The linguistic variable speed ranges between 0 and 300 km/h and includes the fuzzy sets slow, very slow, fast, …

Fuzzy sets define the linguistic values.

Hedges are qualifiers of a linguistic variable.

All purpose: very, quite, extremely

Probability: likely, unlikely

Quantifiers: most, several, few

Possibilities: almost impossible, quite possible

TRUTH TABLES

Truth tables define logic functions of two propositions. Let X  and Y be two propositions, either of which can be true or false.

The operations over the propositions are:

Conjunction (Ù): X AND Y.

Disjunction (Ú): X OR Y.

Implication or conditional (Þ):            IF X THEN Y.

Bidirectional or equivalence (Û): X IF AND ONLY IF Y.

FUZZY RULES

A fuzzy rule is defined as the conditional statement of the form

If x is A

THEN y is B

where x and y are linguistic variables and A and B are linguistic values determined by fuzzy sets on the universes of discourse X and Y.

The decision-making process is based on rules with   sentence conjunctives AND, OR and ALSO.

Each rule corresponds to a fuzzy relation.

Rules belong to a rule base.

Example: If (Distance x to second car is SMALL) OR (Distance y to obstacle is CLOSE) AND (speed v is HIGH) THEN (perform LARGE correction to steering angle q) ALSO (make MEDIUM reduction in speed v).

Three antecedents (or premises) in this example give rise to two outputs (consequences).

FUZZY INFERENCE SYSTEMS (FIS)

Fuzzy rule based systems, fuzzy models, and fuzzy expert systems are also known as fuzzy inference systems.

The key unit of a fuzzy logic system is FIS.

The primary work of this system is decision-making.

FIS uses “IF...THEN” rules along with connectors “OR” or “AND” for making necessary decision rules.

The input to FIS may be fuzzy or crisp, but the output from FIS is always a fuzzy set.

When FIS is used as a controller, it is necessary to have crisp output.

Hence, there should be a defuzzification unit for converting fuzzy variables into crisp variables along FIS.

There are two types of Fuzzy Inference Systems:

Mamdani FIS(1975)

Sugeno FIS(1985)

MAMDANI FUZZY INFERENCE SYSTEMS (FIS)

Fuzzify input variables:

Determine membership values.

Evaluate rules:

Based on membership values of (composite) antecedents.

Aggregate rule outputs:

Unify all membership values for the output from        all rules.

Defuzzify the output:

COG: Center of gravity (approx. by summation).

SUGENO FUZZY INFERENCE SYSTEMS (FIS)

The main steps of the fuzzy inference process namely,

fuzzifying the inputs and

applying the fuzzy operator are exactly the same as in MAMDANI FIS.

The main difference between Mamdani’s and Sugeno’s methods is that Sugeno output membership functions are either linear or constant.

FUZZY EXPERT SYSTEMS

An expert system contains three major blocks:

Knowledge base that contains the knowledge specific to the domain of application.

Inference engine that uses the knowledge in the knowledge base for performing suitable reasoning for user’s queries.

User interface that provides a smooth communication between the user and the system.

Fuzzy Inference Processing

1.There are three models for Fuzzy processing based on the expressions of consequent parts in fuzzy rules

Suppose xi are inputs and y is the consequents in fuzzy rules

Mamdani Model: y = A 

where A is a fuzzy number to reflect fuzziness

Though it can be used in all types of systems, the model is more suitable for knowledge processing systems than control systems

2. TSK (Takagi-Sugano-Kang) model:       

y = a0 + Ʃ ai xi     where ai are constants

The output is the weighted linear combination of input variables  (it can be expanded to nonlinear combination of input variables)

Used in fuzzy control applications

3. Simplified fuzzy model: y = c

where c is a constant

Thus consequents are expressed by constant values