Intelligent web applications

Intelligent Applications for Web
Priti Srinivas Sajja
Associate Professor
Department of Computer Science
Sardar Patel University

Visit priti sajja.info for detail

Created By Priti Srinivas Sajja 1


Artificial
Intelligence
• Name: Dr. Priti Srinivas Sajja
Bio-inspired • Communication:
• Email : priti_sajja@yahoo.com
Web • Mobile : +91 9824926020
• URL :http://pritisajja.info
Web Intelligence
• Academic qualifications : Ph. D in Computer Science
Searching and
Retrieval • Thesis title: Knowledge-Based Systems for Socio-
Knowledge • Economic Development (2000)
Management on Web
• Subject area of specialization : Artificial Intelligence
Web Mining
• Publications : 109 in Books, Book Chapters, Journals and
Agent Based in Proceedings of International and National Conferences
Web

Acknowledgement
2
Created By Priti Srinivas Sajja


Artificial
Introduction Natural Intelligence
Intelligence
• Responds to situations flexibly.
Bio-inspired • Makes sense of ambiguous or erroneous messages.
• Assigns relative importance to elements of a situation.
• Finds similarities even though the situations might be
Web different.
• Draws distinctions between situations even though there may
Web Intelligence be many similarities between them.

Searching and
Retrieval Artificial Intelligence
Knowledge
• According to Rich & Knight (1991) “AI is the study of how to make
Management on Web computers do things, at which, at the moment, people are
better”.
Web Mining
• A machine is regarded as intelligent if it exhibits human
Agent Based characteristics generated through natural intelligence.
Web
• AI is the study of human thought processes and moving toward
problem solving in a symbolic and non-algorithmic way.
Acknowledgement
3


Artificial
Introduction
Intelligence

Bio-inspired

Web

Web Intelligence

Searching and
Retrieval
“Artificial Intelligence(AI) is the study of how
Knowledge
to make computers do things at which,
Management on Web at the moment, people are better”
Web Mining
• Elaine Rich, Artificial Intelligence,
Agent Based McGraw Hill Publications, 1986
Web

Acknowledgement
4


Artificial
Introduction
Intelligence human thought process heuristic methods
Bio-inspired where people are better non-algorithmic

Web
characteristics we knowledge using
associate with intelligence symbols
Web Intelligence
Constituents of artificial intelligence
Searching and
Retrieval
Knowledge
Management on Web
Acceptable solution Extreme solution, either best or
Web Mining
in acceptable time worst taking  (infinite) time

Agent Based time
Web
Nature of AI solutions
Acknowledgement
5


Artificial
AI Tests
Intelligence Testing Intelligence Turing test will fail to test
for intelligence in two
circumstances;
Bio-inspired
1. A machine may well be
Can you tell intelligent without
Web me what is
222222*67344
?
being able to chat
exactly like a human; and;
Web Intelligence Why
Sir?
2. The test fails to capture
Searching and the general properties of
Retrieval intelligence, such as the
ability to solve difficult
Knowledge
The Boss could not judge who was replying, problems or come up with
Management on Web
thus the machine is as intelligent as the original insights. If a
secretary.
Web Mining machine can solve a
difficult problem that
Agent Based The Turing test
no person could solve,
Web
it would, in principle, fail
Acknowledgement the test.
6


Artificial Can you find any test to check the given system is intelligent or not?
AI Tests
Intelligence
Walks,
Bio-inspired Makes and perceives, tests,
understands joke smells, and
feels like
Web human
Reacts
differently
Web Intelligence
Solves your
Searching and problem
If it talks
Retrieval like human
Knowledge
Management on Web

Web Mining
Translates,
Agent Based
conceptually form a test summarizes,
Web and use it in different situation and learns
before accepting it.
Acknowledgement
7


Artificial Rich & Knight (1991) classified and described the different areas that
Applications
Intelligence Artificial Intelligence techniques have been applied to as follows:

Bio-inspired

Web Mundane Tasks Expert Tasks
• Perception - vision and • Engineering - design, fault
Web Intelligence speech finding, manufacturing
• Natural language planning, etc.
Searching and understanding, generation, • Scientific analysis
Retrieval and translation
• Medical diagnosis
• Commonsense reasoning
Knowledge • Financial analysis
Management on Web • Robot control Formal Tasks
• Games - chess,
Web Mining backgammon, checkers, etc.
• Mathematics- geometry,
Agent Based logic, integral calculus,
Web theorem proving, etc.

Acknowledgement
8


Artificial
Data Pyramid
Intelligence
IS
Bio-inspired
Strategy makers apply morals,
WBS Wisdom (experience)
principles, and experience to generate
Web policies

Higher management generates Knowledge (synthesis)
KBS
knowledge by synthesizing
Web Intelligence information

Middle management uses reports/info. DSS, MIS Information (analysis)
Searching and generated though analysis and acts
accordingly
Retrieval
TPS Data (processing of raw observations )
Knowledge Basic transactions by operational
Management on Web staff using data processing

Web Mining Volume Sophistication
and complexity
Agent Based
Web Data pyramid

Acknowledgement
9


Knowledge
Artificial
Based systems
Intelligence Knowledge Inference
base engine
Explanation Self-
Bio-inspired
and learning
reasoning User interface
Web

Web Intelligence

Searching and General structure of KBS
Retrieval
Knowledge According to the classifications by Tuthhill & Levy (1991), five main types
Management on Web of KBS exists:
 Expert systems
Web Mining
 Linked Systems
Agent Based  CASE based Systems
Web  Intelligent Tutoring Systems
 Intelligent User Interface for Database
Acknowledgement
10


Knowledge
Artificial
Based systems
Intelligence
Experience
Experts
Bio-inspired
Sources of Satellite
Web Broadcasting
(Internet, TV,
Printed knowledge and Radio)
Media
Web Intelligence
Types of Knowledge
Searching and
• Tacit knowledge
Retrieval
• Explicit knowledge
Knowledge
Management on Web • Commonsense knowledge
• Informed commonsense knowledge
Web Mining
• Heuristic knowledge
Agent Based • Domain knowledge
Web
• Meta knowledge
Acknowledgement
11


Artificial
Pros and Cons  Intelligence, explanation and reasoning
Intelligence
 Partial self learning, uncertainty handling
Bio-inspired  Documentation of knowledge
Web
 Proactive problem solving
 Cost effectiveness
Web Intelligence

Searching and
Retrieval  Nature of knowledge
Knowledge  Large volume of knowledge
Management on Web
 Knowledge acquisition techniques
Web Mining  Little support to engineer AI based systems
Agent Based  Shelf life of knowledge and system
Web  Development Effort
Acknowledgement
12


Artificial
Intelligence Bio-Inspired Computing
Bio-inspired
Bio-inspired  New approaches to AI
 Taking inspiration form nature and biological systems
Web  Includes models such as
 Artificial Neural Network (ANN),
Web Intelligence  Genetic Algorithm(GA),
Searching and  Swarm Intelligence(SI), etc.
Retrieval  Nature has virtues of self learning, evolution,
Knowledge emergence and immunity
Management on Web
 The objective of bio-inspired models and techniques to
Web Mining take inspiration from Mother Nature and solve
Agent Based
problems in more effective and intelligent way
Web

Acknowledgement
13


Artificial
Intelligence Artificial Neural Network (ANN)
Bio-inspired
Bio-inspired  An artificial neural network (ANN) is connectionist model of
programming using computers.
Web  An ANN attempts to give computers humanlike abilities by
mimicking the human brain’s functionality.
Web Intelligence  The human brain consists of a network of more than a hundred
billions interconnected neurons working in a parallel fashion.
Searching and
Retrieval W1
X1
Knowledge
Management on Web X2 W2 XiWi y
… ….
W
Web Mining
n
Xn
Agent Based
Web A biological neuron An artificial neuron

Acknowledgement
14


Artificial
Intelligence
A Perceptron
Bio-inspired
Bio-inspired

Web

Web Intelligence Multilayer Neural Network
Searching and Input layer Hidden layers
Retrieval
X1 W12 Output layer
Knowledge X2
Management on Web O0
X3 . . .
. . O1
Web Mining . .
. . . . ….
. . . . Om
Agent Based .
Web
Xn
W1h
Acknowledgement
15


Artificial Genetic Algorithms (GA)
Intelligence
• It mimics Nature’s evolutionary approach
Bio-inspired
Bio-inspired • The algorithm is based on the process of natural selection—
Charles Darwin’s “survival of the fittest.”
Web
• GAs can be used in problem solving, function optimizing,
machine learning, and in innovative systems.
Web Intelligence Start with initial population
by randomly selected Initial population
Individuals
Searching and
Modify
Retrieval with Selection Crossover Mutation
Knowledge operations
Management on Web Evaluate
fitness of new Evaluating new individuals through fitness function
Web Mining individuals

Update population with
Agent Based better individuals and Modify the population
Web repeat

Genetic cycle
Acknowledgement
16


Artificial
Intelligence Swarm Intelligence
Bio-inspired
Bio-inspired  Inspired by the collective behavior of social insect
colonies and other animal societies
Web  Ant colony, fish school, bird flocking and honey comb
are the examples
Web Intelligence

Searching and
Retrieval
Knowledge
Management on Web

Web Mining

Agent Based
Web

Acknowledgement
17


Artificial Some more examples ….
Intelligence

Bio-inspired
Bio-inspired

Web

Web Intelligence

Searching and
Retrieval
Knowledge
Management on Web

Web Mining

Agent Based
Web

Acknowledgement
18


Artificial
Intelligence

Bio-inspired

Web
Web

Web Intelligence

Searching and
Retrieval
Knowledge
Management on Web

Web Mining

Agent Based
Web

Acknowledgement
19


Artificial • Internet can be defined as network of networks.
Intelligence
• The World Wide Web (WWW or Web) is a large scale
Bio-inspired
distributed hypermedia system on the internet
Web platform.
Web
• The WWW is based on the HTTP-protocol for data
Web Intelligence transfer, HTML markup for content display on top of the
Internet infrastructure that uses different protocols and
Searching and
Retrieval content description schemes.
Knowledge • According to Hans-Georg Stork (2002), the Web is
Management on Web experiencing two issues:
Web Mining • Not able for “semantic” access and use problem
Agent Based • Depends on the universality of physical access via
Web high-bandwidth local loops and broadband wireless
channels.
Acknowledgement
20


Artificial
Intelligence • Semantic Web is an extension of the current Web in
Bio-inspired which information is given well defined meaning by
associating metadata. (Berners-Lee, Hendler, & Lassila,
Web
Web 2001).

Web Intelligence • Basic objective of a semantic web is “Making content
Searching and machine-understandable”.
Retrieval
Knowledge • The semantic web aims to allow Web entities (software
Management on Web agents, users, and programs) for interoperating, dynamically
discovering and using resources, extracting knowledge, and
Web Mining
solving complex problems.
Agent Based
Web

Acknowledgement
21


Artificial Challenges and limitations of the current Web
Intelligence  Lack of knowledge-based searches
Bio-inspired  Lack of effective techniques to access the Web in depth
 Lack of mechanisms to deal with dynamic requirements of users
Web  Lack of automatically constructed directories
Web  Lack of multi-dimensional analysis and data mining support
Web Intelligence By employing the AI techniques for web functions, it is possible to
Intelligence
Searching and partly impart intelligence in web-based business.
Retrieval
AI Techniques Web Technology
Knowledge
Management on Web
• Platform of Internet
• Knowledge representation • Protocols and standards
Web Mining • Knowledge management • Browser
• Expert system • Search engine Web
• Heuristic functions • Semantic Web Intelligence
Agent Based • New AI methods • Other software
Web
The Web Intelligence (WI) is considered as employment of AI techniques for
Acknowledgement the Web.
22


Artificial Semantic Web Social Search Engine Web Knowledge
 Search Engine Intelligence Techniques Management
Intelligence  Ontology  Popular tools  Customized  Knowledge
management and techniques searches management
Bio-inspired  Meta ontology  Social Network  Meta search architecture for
 Interoperability Analysis engine Web
 Inference  Search engine  Security
optimization
Web

Web
Web Intelligence
Intelligence
Searching and
Retrieval
Knowledge
Management on Web Web Information Web Mining Web Agents Human Computer
Retrieval  Web log mining Interaction/NLP
 Intelligent
Web Mining  Information  Web structure  Personalized
agents
retrieval and mining interface
 Multi agent
filtering  Web content  Multi lingual
Agent Based systems
 Performance mining interfaces
 Pattern
Web measures  Sensor Web  Usability
discovery
 NLP mining

Acknowledgement
23


Artificial To implement a simple Web crawler following steps can
Intelligence be performed.

Bio-inspired 1. Start interaction with user and seek keywords
and URL to start with
Web 2. Add the URL to list to search for
3. Repeat while list is not empty
Web Intelligence 3.1 Consider the first URL and mark with appropriate flag
Searching and 3.2 If the protocol of the selected URL is not HTTP then
Searching break
Retrieval
Knowledge 3.3 Follow the robot.txt file (instructions), if any
Management on Web 3.4 Open the URL
3.5 If the URL is not an HTML file then break else add the
Web Mining
file into list of files found
Agent Based 3.6 Extract links by traversing the file
Web
3.7 Repeat this procedure for every link within the file
Acknowledgement
24


Artificial
Intelligence

Bio-inspired
Spider
Lists Index Processing Storage
Web

Web Intelligence Web crawler process
Searching and
Searching
Retrieval
Simple Crawler
Knowledge Searching all pages
Management on Web
Focused Crawler
Web Mining Searching relevant pages

Agent Based
Web
Web
Scope of focused crawler
Acknowledgement
25


Artificial Information Retrieval (IR) is a science of
Intelligence • information finding,
Bio-inspired • acquiring,
• storing and
Web • utilizing the information for problem solving.

Web Intelligence
The formal steps are given as follows:
Information
Searching and • Indexing
Retrieval
Retrieval • Query formulation
Knowledge • Matching query
Management on Web representation
Web Mining • Relevance feedback and
• interactive retrieval
Agent Based
Web

Acknowledgement
26


Artificial
Intelligence
Models of Information Retrieval

Bio-inspired 1. Boolean Model - Boolean operators like AND, OR
and NOT are applied to retrieve content.
Web
2. Vector space model - represents the documents
and queries as vectors (defined by keywords) in a
Web Intelligence
space having more than one dimensions.
Information
Searching and
Retrieval
Retrieval 3. Probabilistic model - considers the retrieved
content according to some rank based on some
Knowledge
Management on Web probability.

Web Mining 4. Latent semantic model - considers associations
among terms and documents to retrieve required
Agent Based content.
Web

Acknowledgement
27


Artificial
Intelligence

Bio-inspired User Terminology Grammar Lexicon Token Web
Templates
Preprocessing Tokenizer Recognizer Parser Interpreter
Web
Web Search
Natural Query Interpreted Request
Web Intelligence Query
Dialog Processor Search Mechanism
Searching and
NLP for IR Filtered Result Search Result
Search Result
Retrieval
Knowledge User Profile and
Management on Web Dialog Context Model Local information
Analyzer and and Domain
Generator Terminology
Web Mining

Agent Based Generic NLP architecture
Web

Acknowledgement
28


Artificial
Intelligence Research Trend in IR
Bio-inspired
• Heuristic filtering
Web
• Semantic Information
Web Intelligence
• Multimedia Data
Researchand
Searching Trend
RetrievalIR
in • Opinion Retrieval
Knowledge • Information retrieval and translation
Management on Web
• Fuzzy Boolean model of information retrieval
Web Mining

Agent Based
Web

Acknowledgement
29


Artificial
Intelligence The Web follows document-centric approach,
which lacks efficient representation and access of
Bio-inspired
the content on Web.
Web
Knowledge Knowledge
Web Intelligence Sources Use Engineer

Searching and
Retrieval Discover Knowledge Document
Knowledge Base
Knowledge
Management Web
Management on

Organizational Share Standards,
Web Mining
Requirements Protocols, and
Agent Based Services
Web
Typical Knowledge Management Process
Acknowledgement
30


Artificial
Intelligence

Bio-inspired Service Crawler Inference Knowledge
Web
Standards mechanism Discovery

Web

Metadata Knowledge Domain Knowledge
Web Intelligence repository Ontology Processing

Searching and
Experts
Retrieval Knowledge
Editor Local User Profiles
Presentation
Knowledge
Knowledge Administrator Documents
Management Web
Management on

Web Mining
Users
Agent Based
Web
Knowledge Management Architecture on the Web
Acknowledgement
31


Artificial
Intelligence Knowledge Management on Web
Bio-inspired
• Autonomous agents for knowledge discovery
Web
• Protocols for knowledge share and use
Web Intelligence
• Ontology editors
Searching and
Retrieval
• K-Commerce
Knowledge
Knowledge • Knowledge management models
Management Web
Management on
• Virtual world
Web Mining
• Wisdom Web
Agent Based
Web

Acknowledgement
32


Artificial
Intelligence
Data Mining
 The data mining techniques are dedicated techniques that
Bio-inspired extract patterns and useful information from the
Web
existing known sources of data.

Text Mining
Web Intelligence
 Text mining techniques are used to find, organize and discover
Searching and information from the textual resources.
Retrieval
Knowledge Web Mining
Management on Web
 Web mining techniques are used to find, organize and discover
WebMining
Web Mining information from the huge unstructured
Agent Based platform such as Web.
Web

Acknowledgement
33


Artificial
Intelligence
Challenges of Web Mining
 Structure highly unstructured
Bio-inspired
 Size tremendous
Web
 Nature dynamic
Web Intelligence  Accessibility global by anybody
Searching and  Redundant similar information in
Retrieval many formats
Knowledge
Management on Web  Noise virus, malware and adware
WebMining
Web Mining

Agent Based
Web

Acknowledgement
34


Artificial Purpose
Intelligence
Data Text Web
Bio-inspired Finding
pattern and Mining Mining Mining
knowledge
Web Data Information Web
Finding Retrieval Retrieval Retrieval
relevant
data Data Type
Web Intelligence
/Sources
Any data Textual data Web data
Searching and
Retrieval Web Mining and Other Related Activities
Knowledge
Management on Web

WebMining
Web Mining Web Mining
Agent Based Web
Web Content Web Log
Structure
Web Mining Mining
Mining

Acknowledgement
35


Artificial
Intelligence
Web Content Mining
 It attempts to mine content of the Web to discover
Bio-inspired
useful patterns through hyperlinks.
Web  The content may be text, images, audio, video, and
structured data like tables and graphs.
Web Intelligence
 The web content mining goes beyond keyword
Searching and extraction and requires advanced techniques such as
Retrieval
NLP and AI.
Knowledge
Management on Web  Web content mining strategies are of two groups
 one that directly mine the content of documents and
WebMining
Web Mining
 second that improves on the content search of other tools
Agent Based like search engines.
Web

Acknowledgement
36


Artificial Classification as Web Content Mining Techniques
Intelligence
 Classification : deals with classification of the content into various
Bio-inspired groups as accurate as possible. The training sets and test
(validation) sets are provided to the classification algorithm to build
Web and to test the classification model respectively.

Typical classification techniques include:
Web Intelligence
 Decision tree based methods;
Searching and
 Rule base classification;
Retrieval
 Supervised learning through artificial neural network;
Knowledge
Management on Web  Evolutionary techniques; and
 Support vector machines;
WebMining
Web Mining

Agent Based
Web

Acknowledgement
37


Artificial Clustering as Web Content Mining Techniques
Intelligence
 Clustering : deals with finding groups of similar objects based on
Bio-inspired the content characteristics itself in unsupervised approach.

Web
3
1 Partition
Web Intelligence
Clustering
1, 2 and 3 are
Searching and Initial Points 2 independent
Retrieval clusters.

Knowledge
Management on Web 3
1
WebMining
Web Mining Hierarchical
Clustering
Here cluster 3 is
Agent Based subset of 2; and 2
2
Web is subset of 1.

Partition and hierarchical clustering
Acknowledgement
38


Artificial Classification and Clustering as Web Content Mining
Intelligence
Techniques
Bio-inspired  Association Mining : deals with discovering interesting
relations between variables in large databases. This
Web technique find rules that will predict the occurrence of an entity
based on general pattern exists in the given data sets.
Web Intelligence
 Consider following example.
Searching and
Retrieval
Transaction ID Bread Cheese Sauce
Knowledge
Management on Web 1 Yes Yes Yes

2 No No Yes
WebMining
Web Mining
3 No Yes Yes
Agent Based
Web

Acknowledgement
39


Artificial Classification as Web Content Mining Techniques
Intelligence
 Opinion Mining: deals with extraction of opinion of users
Bio-inspired learn attitude of the content, person or product. Opinion mining
plays an important role in mining applications for customer relationship
Web management, consumer attitude detection, brand and product positioning,
product reviews, and market research.
Web Intelligence
 Feature based opinion mining mines the Web content
Searching and by given features of a specified product/entity.
Retrieval
 Once the opinions are collected, they are further grouped
Knowledge
Management on Web and analyzed.

WebMining
Web Mining

Agent Based
Web

Acknowledgement
40


Artificial
Intelligence
Some other Web Content Mining Techniques
 Structured data extraction: Structured data extraction deals with
Bio-inspired extraction of important information about product, services
and data records that are available in structured form on host
Web
pages.
 Unstructured content extraction: It deals with extraction of
Web Intelligence
content that is not available in structured form.
Searching and  Web information integration: It extracts content form multiple
Retrieval site, checks for redundancy, and integrates information. Vice versa,
Knowledge the content mining can be used for web site classification/clustering
Management on Web
also.
WebMining
Web Mining  Detecting noise: The malware, adware and virus from multiple site
can be identified and blocked.
Agent Based
 Opinion mining: The customer surveys, opinion, sentiments and
Web
product review information etc can be extracted here.
Acknowledgement
41


Artificial
Intelligence
Web Usage Mining
 The Web usage mining provides the collection of
Bio-inspired
information accessed so far to its users.
Web  Web usage mining highlights the behavior of users on
the Web and understands access patterns and trends.
Web Intelligence  The web usage mining deals with web log and
Searching and
accumulated data on web servers in order to
Retrieval understand the user behavior and the web structure.
Knowledge  There are two main purposes for web usage mining.
Management on Web The first one is to track general access pattern and
WebMining
Web Mining second is customized usage tracking.
Agent Based
Web

Acknowledgement
42


Artificial
Intelligence

Bio-inspired Retrieval
Cleaning
Web Log Data
Identification
Web Intelligence Integration
Registration Cleaning
Data Noise Use
Searching and Pattern Integration
discovery & Analysis
Retrieval Other Cleaning of Patterns Analysis
and
Information Malware Discovery and Use
Knowledge Analysis
Management on Web

WebMining
Web Mining
Activities for web usage mining
Agent Based
Web

Acknowledgement
43


Artificial
Intelligence
Web Structure Mining
Bio-inspired
 The Web behaves like a hypertext document
information system. The Web objects such as pages and
Web sites are generally exist between the numbers of links.
 Web structure mining focuses with structure of such
Web Intelligence
hyperlinks on the Web.
Searching and  There are two basic techniques to analyze the network of links
Retrieval
on the Web. These methods are
Knowledge
Management on Web (i) Hyperlinked Induced Topic Search (HITS) concept and
(ii) Page Rank method.
WebMining
Web Mining
 The Web may be represented as a huge directed graph
Agent Based
Web
structure.

Acknowledgement
44


Artificial
Intelligence
Sensor Web Mining
 Data are collected from different sensors placed at remote
Bio-inspired places.
 Provides opportunity of efficient geo-referencing in
Web
remote fashion.
Web Intelligence  Sensor Web consists number of sensor
platforms called pods.
Searching and
 Each pod senses some dynamic Sensor Suit
Retrieval
environmental data in real time fashion. Memory
Knowledge
Management on Web  Radio is used to connect the pod with its Microcontroller Radio

local neighborhood. Solar panel

WebMining
Web Mining  Applications are weather forecasting,
Agent Based costal area monitoring, communication
Web and education, and eco-system
Architecture of a Pod
information and management.
Acknowledgement
45


Artificial
Intelligence

Bio-inspired

Web

Web Intelligence

Searching and
Retrieval
Knowledge
Management on Web

WebMining
Web Mining

Agent Based
Web

Acknowledgement
46


Artificial
Intelligence AI for Web Mining
Bio-inspired  Mining pro-active agents

Web
 ANN for finding/ analyzing patterns
Web Intelligence
 Fuzzy partitions and clustering
Searching and
Retrieval
Knowledge  Evolution of patterns from Web
Management on Web

WebMining
Web Mining  Heuristic based filtering functions for mining
Agent Based
Web
 Sentiment mining using NLP on social network
Acknowledgement platform
47


Artificial
Intelligence Sensors to acquire environmental
information and user’s
requirement
Bio-inspired
Autonomy Mobility

Agent
Web
Cooperation Proactivity
Action interface
Web Intelligence

Searching and
Retrieval Learning

Knowledge
Management on Web Types of Agents
 Collaborative Agent  Information Agent
Web Mining
 Interface Agent  Intelligent Agent
Agent Based
Agent Based
 Mobile Agent  Hybrid Agent
Web
Web

Acknowledgement
48


Artificial
Intelligence
Filtering Agent
Interface
Bio-inspired Agent

Browsers Document
Web URL Management
Management Agent
Query Agent
Web Intelligence Web/ Search Engine
Semantic Agent
Protocols and
Searching and Standards Web
Internet
Retrieval
Knowledge Ontology Tools Core Social Networking
Management on Web Ontology Agent Services Agent

Customized
Web Mining Services

Agent Based
Agent Based
Web
Web Agent based web

Acknowledgement
49


Artificial
Intelligence
Web
Bio-inspired
Local Databases Base Domain Databases Base
Web
Ontology Knowledge Base
Web Intelligence

Searching and Query Manager Search Engine Visualization
Retrieval
Knowledge
Management on Web Web Bowser

Web Mining Client Client Client

Agent Based
Agent Based
Web
Web Figure 11.10 Information retrieval agent

Acknowledgement
50


Artificial
Intelligence

Bio-inspired  Agent for semantic analysis
Web  Verification and validation (V&V) agent
 Finding suitable web services agent
Web Intelligence
 Crawler agent
Searching and
Retrieval  Explanation and reasoning Agent
Knowledge  Natural language interface agent
Management on Web
 Communication agent
Web Mining
 Network traffic management agent
Agent Based
Agent Based
Web
Web  Mobile agent for personalized content
Acknowledgement representation
51


Artificial
Intelligence Major References
 Knowledge-based systems, Akerkar RA and Priti Srinivas Sajja, Jones & Bartlett
Bio-inspired Publishers, Sudbury, MA, USA (2009)
 Intelligent technologies for web applications”, Priti Srinivas Sajja, Rajendra
Web Akerkar; CRC Press (Taylor & Francis Group), Boka Raton, FL, USA (2012)

Web Intelligence
Other References
 llustrationsOf.com
Searching and  coders-view.blogspot.com
Retrieval  info.ideal.com
Knowledge  http://businessintelligencetalk.blogspot.in
Management on Web  www.gadgetcage.com
 Engadget.com
Web Mining  scenicreflections.com
 lih.univ-lehavre.fr
Agent Based
 business2press.com
Web
 globalswarminghoneybees.blogspot.com
Acknowledgement  pritisajja.info
52


Artificial
Intelligence

Bio-inspired

Web

Web Intelligence

Searching and
Retrieval
Knowledge
Management on Web To the participants and authority of the
AICTE sponsored Staff Development Programme on Data Mining,
Web Mining 16-28 April, 2012
Agent Based at the
Web L. J. Institute of Engineering & Technology, Ahmedabad.
Acknowledgement
53

Intelligent web applications

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à Intelligent web applications

Similaire à Intelligent web applications (20)

Plus de Priti Srinivas Sajja

Plus de Priti Srinivas Sajja (10)

Dernier

Dernier (20)

Intelligent web applications