Intelligent web applications


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This lecture has been taken for teh AICTE sponsored workshop on web mining. It covers infromation retrieval, searching, meta search engine, focused search engine, web mining, agent based web, knowledge management on web, ontology management systems and wisom web.

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Intelligent web applications

  1. 1. Intelligent Applications for Web Priti Srinivas Sajja Associate Professor Department of Computer Science Sardar Patel University Visit priti for detail Created By Priti Srinivas Sajja 1
  2. 2. Intelligent Applications for WebArtificialIntelligence • Name: Dr. Priti Srinivas SajjaBio-inspired • Communication: • Email : priti_sajja@yahoo.comWeb • Mobile : +91 9824926020 • URL :http://pritisajja.infoWeb Intelligence • Academic qualifications : Ph. D in Computer ScienceSearching andRetrieval • Thesis title: Knowledge-Based Systems for Socio-Knowledge • Economic Development (2000)Management on Web • Subject area of specialization : Artificial IntelligenceWeb Mining • Publications : 109 in Books, Book Chapters, Journals andAgent Based in Proceedings of International and National ConferencesWebAcknowledgement 2 Created By Priti Srinivas Sajja
  3. 3. Intelligent Applications for Web ArtificialIntroduction Natural IntelligenceIntelligence • 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 beWeb different. • Draws distinctions between situations even though there mayWeb Intelligence be many similarities between them.Searching andRetrieval Artificial IntelligenceKnowledge • According to Rich & Knight (1991) “AI is the study of how to makeManagement on Web computers do things, at which, at the moment, people are better”.Web Mining • A machine is regarded as intelligent if it exhibits humanAgent 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 Created By Priti Srinivas Sajja
  4. 4. Intelligent Applications for Web ArtificialIntroductionIntelligenceBio-inspiredWebWeb IntelligenceSearching andRetrieval “Artificial Intelligence(AI) is the study of howKnowledge 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, 1986WebAcknowledgement 4 Created By Priti Srinivas Sajja
  5. 5. Intelligent Applications for Web ArtificialIntroductionIntelligence human thought process heuristic methodsBio-inspired where people are better non-algorithmicWeb characteristics we knowledge using associate with intelligence symbolsWeb Intelligence Constituents of artificial intelligenceSearching andRetrievalKnowledgeManagement on Web Acceptable solution Extreme solution, either best orWeb Mining in acceptable time worst taking  (infinite) timeAgent Based timeWeb Nature of AI solutionsAcknowledgement 5 Created By Priti Srinivas Sajja
  6. 6. Intelligent Applications for WebArtificialAI TestsIntelligence 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 withoutWeb me what is 222222*67344 ? being able to chat exactly like a human; and;Web Intelligence Why Sir? 2. The test fails to captureSearching and the general properties ofRetrieval intelligence, such as the ability to solve difficultKnowledge The Boss could not judge who was replying, problems or come up withManagement on Web thus the machine is as intelligent as the original insights. If a secretary.Web Mining machine can solve a difficult problem thatAgent Based The Turing test no person could solve,Web it would, in principle, failAcknowledgement the test. 6 Created By Priti Srinivas Sajja
  7. 7. Intelligent Applications for WebArtificial Can you find any test to check the given system is intelligent or not?AI TestsIntelligence Walks,Bio-inspired Makes and perceives, tests, understands joke smells, and feels likeWeb human Reacts differentlyWeb Intelligence Solves yourSearching and problem If it talksRetrieval like humanKnowledgeManagement on WebWeb Mining Translates,Agent Based conceptually form a test summarizes,Web and use it in different situation and learns before accepting it.Acknowledgement 7 Created By Priti Srinivas Sajja
  8. 8. Intelligent Applications for WebArtificial Rich & Knight (1991) classified and described the different areas thatApplicationsIntelligence Artificial Intelligence techniques have been applied to as follows:Bio-inspiredWeb Mundane Tasks Expert Tasks • Perception - vision and • Engineering - design, faultWeb Intelligence speech finding, manufacturing • Natural language planning, etc.Searching and understanding, generation, • Scientific analysisRetrieval and translation • Medical diagnosis • Commonsense reasoningKnowledge • Financial analysisManagement 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 Created By Priti Srinivas Sajja
  9. 9. Intelligent Applications for WebArtificialData PyramidIntelligence ISBio-inspired Strategy makers apply morals, WBS Wisdom (experience) principles, and experience to generateWeb policies Higher management generates Knowledge (synthesis) KBS knowledge by synthesizingWeb Intelligence information Middle management uses reports/info. DSS, MIS Information (analysis)Searching and generated though analysis and acts accordinglyRetrieval TPS Data (processing of raw observations )Knowledge Basic transactions by operationalManagement on Web staff using data processingWeb Mining Volume Sophistication and complexityAgent BasedWeb Data pyramidAcknowledgement 9 Created By Priti Srinivas Sajja
  10. 10. Intelligent Applications for WebKnowledgeArtificialBased systemsIntelligence Knowledge Inference base engine Explanation Self-Bio-inspired and learning reasoning User interfaceWebWeb IntelligenceSearching and General structure of KBSRetrievalKnowledge According to the classifications by Tuthhill & Levy (1991), five main typesManagement on Web of KBS exists:  Expert systemsWeb Mining  Linked SystemsAgent Based  CASE based SystemsWeb  Intelligent Tutoring Systems  Intelligent User Interface for DatabaseAcknowledgement 10 Created By Priti Srinivas Sajja
  11. 11. Intelligent Applications for WebKnowledgeArtificialBased systemsIntelligence Experience ExpertsBio-inspired Sources of SatelliteWeb Broadcasting (Internet, TV, Printed knowledge and Radio) MediaWeb Intelligence Types of KnowledgeSearching and • Tacit knowledgeRetrieval • Explicit knowledgeKnowledgeManagement on Web • Commonsense knowledge • Informed commonsense knowledgeWeb Mining • Heuristic knowledgeAgent Based • Domain knowledgeWeb • Meta knowledgeAcknowledgement 11 Created By Priti Srinivas Sajja
  12. 12. Intelligent Applications for WebArtificialPros and Cons  Intelligence, explanation and reasoningIntelligence  Partial self learning, uncertainty handlingBio-inspired  Documentation of knowledgeWeb  Proactive problem solving  Cost effectivenessWeb IntelligenceSearching andRetrieval  Nature of knowledgeKnowledge  Large volume of knowledgeManagement on Web  Knowledge acquisition techniquesWeb Mining  Little support to engineer AI based systemsAgent Based  Shelf life of knowledge and systemWeb  Development EffortAcknowledgement 12 Created By Priti Srinivas Sajja
  13. 13. Intelligent Applications for WebArtificialIntelligence Bio-Inspired ComputingBio-inspiredBio-inspired  New approaches to AI  Taking inspiration form nature and biological systemsWeb  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 immunityManagement on Web  The objective of bio-inspired models and techniques toWeb Mining take inspiration from Mother Nature and solveAgent Based problems in more effective and intelligent wayWebAcknowledgement 13 Created By Priti Srinivas Sajja
  14. 14. Intelligent Applications for WebArtificialIntelligence Artificial Neural Network (ANN)Bio-inspiredBio-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 andRetrieval W1 X1KnowledgeManagement on Web X2 W2 XiWi y … …. WWeb Mining n XnAgent BasedWeb A biological neuron An artificial neuronAcknowledgement 14 Created By Priti Srinivas Sajja
  15. 15. Intelligent Applications for WebArtificialIntelligence A PerceptronBio-inspiredBio-inspiredWebWeb Intelligence Multilayer Neural NetworkSearching and Input layer Hidden layersRetrieval X1 W12 Output layerKnowledge X2Management on Web O0 X3 . . . . . O1Web Mining . . . . . . …. . . . . OmAgent Based .Web Xn W1hAcknowledgement 15 Created By Priti Srinivas Sajja
  16. 16. Intelligent Applications for WebArtificial Genetic Algorithms (GA)Intelligence • It mimics Nature’s evolutionary approachBio-inspiredBio-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 IndividualsSearching and ModifyRetrieval with Selection Crossover MutationKnowledge operationsManagement on Web Evaluate fitness of new Evaluating new individuals through fitness functionWeb Mining individuals Update population withAgent Based better individuals and Modify the populationWeb repeat Genetic cycleAcknowledgement 16 Created By Priti Srinivas Sajja
  17. 17. Intelligent Applications for WebArtificialIntelligence Swarm IntelligenceBio-inspiredBio-inspired  Inspired by the collective behavior of social insect colonies and other animal societiesWeb  Ant colony, fish school, bird flocking and honey comb are the examplesWeb IntelligenceSearching andRetrievalKnowledgeManagement on WebWeb MiningAgent BasedWebAcknowledgement 17 Created By Priti Srinivas Sajja
  18. 18. Intelligent Applications for WebArtificial Some more examples ….IntelligenceBio-inspiredBio-inspiredWebWeb IntelligenceSearching andRetrievalKnowledgeManagement on WebWeb MiningAgent BasedWebAcknowledgement 18 Created By Priti Srinivas Sajja
  19. 19. Intelligent Applications for WebArtificialIntelligenceBio-inspiredWebWebWeb IntelligenceSearching andRetrievalKnowledgeManagement on WebWeb MiningAgent BasedWebAcknowledgement 19 Created By Priti Srinivas Sajja
  20. 20. Intelligent Applications for WebArtificial • Internet can be defined as network of networks.Intelligence • The World Wide Web (WWW or Web) is a large scaleBio-inspired distributed hypermedia system on the internetWeb platform.Web • The WWW is based on the HTTP-protocol for dataWeb Intelligence transfer, HTML markup for content display on top of the Internet infrastructure that uses different protocols andSearching andRetrieval content description schemes.Knowledge • According to Hans-Georg Stork (2002), the Web isManagement on Web experiencing two issues:Web Mining • Not able for “semantic” access and use problemAgent Based • Depends on the universality of physical access viaWeb high-bandwidth local loops and broadband wireless channels.Acknowledgement 20 Created By Priti Srinivas Sajja
  21. 21. Intelligent Applications for WebArtificialIntelligence • Semantic Web is an extension of the current Web inBio-inspired which information is given well defined meaning by associating metadata. (Berners-Lee, Hendler, & Lassila,WebWeb 2001).Web Intelligence • Basic objective of a semantic web is “Making contentSearching and machine-understandable”.RetrievalKnowledge • The semantic web aims to allow Web entities (softwareManagement on Web agents, users, and programs) for interoperating, dynamically discovering and using resources, extracting knowledge, andWeb Mining solving complex problems.Agent BasedWebAcknowledgement 21 Created By Priti Srinivas Sajja
  22. 22. Intelligent Applications for WebArtificial Challenges and limitations of the current WebIntelligence  Lack of knowledge-based searchesBio-inspired  Lack of effective techniques to access the Web in depth  Lack of mechanisms to deal with dynamic requirements of usersWeb  Lack of automatically constructed directoriesWeb  Lack of multi-dimensional analysis and data mining support Web Intelligence By employing the AI techniques for web functions, it is possible toIntelligenceSearching and partly impart intelligence in web-based business.Retrieval AI Techniques Web TechnologyKnowledgeManagement on Web • Platform of Internet • Knowledge representation • Protocols and standardsWeb Mining • Knowledge management • Browser • Expert system • Search engine Web • Heuristic functions • Semantic Web IntelligenceAgent Based • New AI methods • Other softwareWeb The Web Intelligence (WI) is considered as employment of AI techniques forAcknowledgement the Web. 22 Created By Priti Srinivas Sajja
  23. 23. Intelligent Applications for WebArtificial Semantic Web Social Search Engine Web Knowledge  Search Engine Intelligence Techniques ManagementIntelligence  Ontology  Popular tools  Customized  Knowledge management and techniques searches managementBio-inspired  Meta ontology  Social Network  Meta search architecture for  Interoperability Analysis engine Web  Inference  Search engine  Security optimizationWebWeb Web IntelligenceIntelligenceSearching andRetrievalKnowledgeManagement on Web Web Information Web Mining Web Agents Human Computer Retrieval  Web log mining Interaction/NLP  IntelligentWeb Mining  Information  Web structure  Personalized agents retrieval and mining interface  Multi agent filtering  Web content  Multi lingualAgent Based systems  Performance mining interfaces  PatternWeb measures  Sensor Web  Usability discovery  NLP miningAcknowledgement 23 Created By Priti Srinivas Sajja
  24. 24. Intelligent Applications for WebArtificial To implement a simple Web crawler following steps canIntelligence be performed.Bio-inspired 1. Start interaction with user and seek keywords and URL to start withWeb 2. Add the URL to list to search for 3. Repeat while list is not emptyWeb Intelligence 3.1 Consider the first URL and mark with appropriate flagSearching and 3.2 If the protocol of the selected URL is not HTTP thenSearching breakRetrievalKnowledge 3.3 Follow the robot.txt file (instructions), if anyManagement on Web 3.4 Open the URL 3.5 If the URL is not an HTML file then break else add theWeb Mining file into list of files foundAgent Based 3.6 Extract links by traversing the fileWeb 3.7 Repeat this procedure for every link within the fileAcknowledgement 24 Created By Priti Srinivas Sajja
  25. 25. Intelligent Applications for WebArtificialIntelligenceBio-inspired Spider Lists Index Processing StorageWebWeb Intelligence Web crawler processSearching andSearchingRetrieval Simple CrawlerKnowledge Searching all pagesManagement on Web Focused CrawlerWeb Mining Searching relevant pagesAgent Based WebWeb Scope of focused crawlerAcknowledgement 25 Created By Priti Srinivas Sajja
  26. 26. Intelligent Applications for WebArtificial Information Retrieval (IR) is a science ofIntelligence • information finding,Bio-inspired • acquiring, • storing andWeb • utilizing the information for problem solving.Web Intelligence The formal steps are given as follows:Information Searching and • IndexingRetrieval Retrieval • Query formulationKnowledge • Matching queryManagement on Web representationWeb Mining • Relevance feedback and • interactive retrievalAgent BasedWebAcknowledgement 26 Created By Priti Srinivas Sajja
  27. 27. Intelligent Applications for WebArtificialIntelligence Models of Information RetrievalBio-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 aWeb Intelligence space having more than one dimensions.Information Searching andRetrieval Retrieval 3. Probabilistic model - considers the retrieved content according to some rank based on someKnowledgeManagement on Web probability.Web Mining 4. Latent semantic model - considers associations among terms and documents to retrieve requiredAgent Based content.WebAcknowledgement 27 Created By Priti Srinivas Sajja
  28. 28. Intelligent Applications for WebArtificialIntelligenceBio-inspired User Terminology Grammar Lexicon Token Web Templates Preprocessing Tokenizer Recognizer Parser InterpreterWeb Web Search Natural Query Interpreted RequestWeb Intelligence Query Dialog Processor Search MechanismSearching andNLP for IR Filtered Result Search Result Search ResultRetrievalKnowledge User Profile andManagement on Web Dialog Context Model Local information Analyzer and and Domain Generator TerminologyWeb MiningAgent Based Generic NLP architectureWebAcknowledgement 28 Created By Priti Srinivas Sajja
  29. 29. Intelligent Applications for WebArtificialIntelligence Research Trend in IRBio-inspired • Heuristic filteringWeb • Semantic InformationWeb Intelligence • Multimedia DataResearchandSearching TrendRetrievalIR in • Opinion RetrievalKnowledge • Information retrieval and translationManagement on Web • Fuzzy Boolean model of information retrievalWeb MiningAgent BasedWebAcknowledgement 29 Created By Priti Srinivas Sajja
  30. 30. Intelligent Applications for WebArtificialIntelligence The Web follows document-centric approach, which lacks efficient representation and access ofBio-inspired the content on Web.Web Knowledge KnowledgeWeb Intelligence Sources Use Engineer Searching and Retrieval Discover Knowledge DocumentKnowledge Base KnowledgeManagement Web Management on Organizational Share Standards,Web Mining Requirements Protocols, andAgent Based ServicesWeb Typical Knowledge Management ProcessAcknowledgement 30 Created By Priti Srinivas Sajja
  31. 31. Intelligent Applications for WebArtificialIntelligenceBio-inspired Service Crawler Inference Knowledge Web Standards mechanism DiscoveryWeb Metadata Knowledge Domain KnowledgeWeb Intelligence repository Ontology Processing Searching and Experts Retrieval Knowledge Editor Local User Profiles PresentationKnowledge Knowledge Administrator DocumentsManagement Web Management onWeb Mining UsersAgent BasedWeb Knowledge Management Architecture on the WebAcknowledgement 31 Created By Priti Srinivas Sajja
  32. 32. Intelligent Applications for WebArtificialIntelligence Knowledge Management on WebBio-inspired • Autonomous agents for knowledge discoveryWeb • Protocols for knowledge share and useWeb Intelligence • Ontology editors Searching and Retrieval • K-CommerceKnowledge Knowledge • Knowledge management modelsManagement Web Management on • Virtual worldWeb Mining • Wisdom WebAgent BasedWebAcknowledgement 32 Created By Priti Srinivas Sajja
  33. 33. Intelligent Applications for WebArtificialIntelligence Data Mining  The data mining techniques are dedicated techniques thatBio-inspired extract patterns and useful information from theWeb existing known sources of data. Text MiningWeb Intelligence  Text mining techniques are used to find, organize and discoverSearching and information from the textual resources.RetrievalKnowledge Web MiningManagement on Web  Web mining techniques are used to find, organize and discoverWebMiningWeb Mining information from the huge unstructuredAgent Based platform such as Web.WebAcknowledgement 33 Created By Priti Srinivas Sajja
  34. 34. Intelligent Applications for WebArtificialIntelligence Challenges of Web Mining  Structure highly unstructuredBio-inspired  Size tremendousWeb  Nature dynamicWeb Intelligence  Accessibility global by anybodySearching and  Redundant similar information inRetrieval many formatsKnowledgeManagement on Web  Noise virus, malware and adwareWebMiningWeb MiningAgent BasedWebAcknowledgement 34 Created By Priti Srinivas Sajja
  35. 35. Intelligent Applications for WebArtificial PurposeIntelligence Data Text WebBio-inspired Finding pattern and Mining Mining Mining knowledgeWeb Data Information Web Finding Retrieval Retrieval Retrieval relevant data Data TypeWeb Intelligence /Sources Any data Textual data Web dataSearching andRetrieval Web Mining and Other Related ActivitiesKnowledgeManagement on WebWebMiningWeb Mining Web MiningAgent Based Web Web Content Web Log StructureWeb Mining Mining MiningAcknowledgement 35 Created By Priti Srinivas Sajja
  36. 36. Intelligent Applications for WebArtificialIntelligence Web Content Mining  It attempts to mine content of the Web to discoverBio-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 keywordSearching and extraction and requires advanced techniques such asRetrieval NLP and AI.KnowledgeManagement on Web  Web content mining strategies are of two groups  one that directly mine the content of documents andWebMiningWeb Mining  second that improves on the content search of other toolsAgent Based like search engines.WebAcknowledgement 36 Created By Priti Srinivas Sajja
  37. 37. Intelligent Applications for WebArtificial Classification as Web Content Mining TechniquesIntelligence  Classification : deals with classification of the content into variousBio-inspired groups as accurate as possible. The training sets and test (validation) sets are provided to the classification algorithm to buildWeb 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;KnowledgeManagement on Web  Evolutionary techniques; and  Support vector machines;WebMiningWeb MiningAgent BasedWebAcknowledgement 37 Created By Priti Srinivas Sajja
  38. 38. Intelligent Applications for WebArtificial Clustering as Web Content Mining TechniquesIntelligence  Clustering : deals with finding groups of similar objects based onBio-inspired the content characteristics itself in unsupervised approach.Web 3 1 PartitionWeb Intelligence Clustering 1, 2 and 3 areSearching and Initial Points 2 independentRetrieval clusters.KnowledgeManagement on Web 3 1WebMiningWeb Mining Hierarchical Clustering Here cluster 3 isAgent Based subset of 2; and 2 2Web is subset of 1. Partition and hierarchical clusteringAcknowledgement 38 Created By Priti Srinivas Sajja
  39. 39. Intelligent Applications for WebArtificial Classification and Clustering as Web Content MiningIntelligence TechniquesBio-inspired  Association Mining : deals with discovering interesting relations between variables in large databases. ThisWeb 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 andRetrieval Transaction ID Bread Cheese SauceKnowledgeManagement on Web 1 Yes Yes Yes 2 No No YesWebMiningWeb Mining 3 No Yes YesAgent BasedWebAcknowledgement 39 Created By Priti Srinivas Sajja
  40. 40. Intelligent Applications for WebArtificial Classification as Web Content Mining TechniquesIntelligence  Opinion Mining: deals with extraction of opinion of usersBio-inspired learn attitude of the content, person or product. Opinion mining plays an important role in mining applications for customer relationshipWeb management, consumer attitude detection, brand and product positioning, product reviews, and market research.Web Intelligence  Feature based opinion mining mines the Web contentSearching and by given features of a specified product/entity.Retrieval  Once the opinions are collected, they are further groupedKnowledgeManagement on Web and analyzed.WebMiningWeb MiningAgent BasedWebAcknowledgement 40 Created By Priti Srinivas Sajja
  41. 41. Intelligent Applications for WebArtificialIntelligence Some other Web Content Mining Techniques  Structured data extraction: Structured data extraction deals withBio-inspired extraction of important information about product, services and data records that are available in structured form on hostWeb pages.  Unstructured content extraction: It deals with extraction ofWeb Intelligence content that is not available in structured form.Searching and  Web information integration: It extracts content form multipleRetrieval site, checks for redundancy, and integrates information. Vice versa,Knowledge the content mining can be used for web site classification/clusteringManagement on Web also.WebMiningWeb 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 andWeb product review information etc can be extracted here.Acknowledgement 41 Created By Priti Srinivas Sajja
  42. 42. Intelligent Applications for WebArtificialIntelligence Web Usage Mining  The Web usage mining provides the collection ofBio-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 andSearching and accumulated data on web servers in order toRetrieval 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 andWebMiningWeb Mining second is customized usage tracking.Agent BasedWebAcknowledgement 42 Created By Priti Srinivas Sajja
  43. 43. Intelligent Applications for WebArtificialIntelligenceBio-inspired Retrieval CleaningWeb Log Data IdentificationWeb Intelligence Integration Registration Cleaning Data Noise UseSearching and Pattern Integration discovery & AnalysisRetrieval Other Cleaning of Patterns Analysis and Information Malware Discovery and UseKnowledge AnalysisManagement on WebWebMiningWeb Mining Activities for web usage miningAgent BasedWebAcknowledgement 43 Created By Priti Srinivas Sajja
  44. 44. Intelligent Applications for WebArtificialIntelligence Web Structure MiningBio-inspired  The Web behaves like a hypertext document information system. The Web objects such as pages andWeb sites are generally exist between the numbers of links.  Web structure mining focuses with structure of suchWeb Intelligence hyperlinks on the Web.Searching and  There are two basic techniques to analyze the network of linksRetrieval on the Web. These methods areKnowledgeManagement on Web (i) Hyperlinked Induced Topic Search (HITS) concept and (ii) Page Rank method.WebMiningWeb Mining  The Web may be represented as a huge directed graphAgent BasedWeb structure.Acknowledgement 44 Created By Priti Srinivas Sajja
  45. 45. Intelligent Applications for WebArtificialIntelligence Sensor Web Mining  Data are collected from different sensors placed at remoteBio-inspired places.  Provides opportunity of efficient geo-referencing inWeb remote fashion.Web Intelligence  Sensor Web consists number of sensor platforms called pods.Searching and  Each pod senses some dynamic Sensor SuitRetrieval environmental data in real time fashion. MemoryKnowledgeManagement on Web  Radio is used to connect the pod with its Microcontroller Radio local neighborhood. Solar panelWebMiningWeb Mining  Applications are weather forecasting,Agent Based costal area monitoring, communicationWeb and education, and eco-system Architecture of a Pod information and management.Acknowledgement 45 Created By Priti Srinivas Sajja
  46. 46. Intelligent Applications for WebArtificialIntelligenceBio-inspiredWebWeb IntelligenceSearching andRetrievalKnowledgeManagement on WebWebMiningWeb MiningAgent BasedWebAcknowledgement 46 Created By Priti Srinivas Sajja
  47. 47. Intelligent Applications for WebArtificialIntelligence AI for Web MiningBio-inspired  Mining pro-active agentsWeb  ANN for finding/ analyzing patternsWeb Intelligence  Fuzzy partitions and clusteringSearching andRetrievalKnowledge  Evolution of patterns from WebManagement on WebWebMiningWeb Mining  Heuristic based filtering functions for miningAgent BasedWeb  Sentiment mining using NLP on social networkAcknowledgement platform 47 Created By Priti Srinivas Sajja
  48. 48. Intelligent Applications for WebArtificialIntelligence Sensors to acquire environmental information and user’s requirementBio-inspired Autonomy Mobility AgentWeb Cooperation Proactivity Action interfaceWeb IntelligenceSearching andRetrieval LearningKnowledgeManagement on Web Types of Agents  Collaborative Agent  Information AgentWeb Mining  Interface Agent  Intelligent AgentAgent BasedAgent Based  Mobile Agent  Hybrid AgentWebWebAcknowledgement 48 Created By Priti Srinivas Sajja
  49. 49. Intelligent Applications for WebArtificialIntelligence Filtering Agent InterfaceBio-inspired Agent Browsers DocumentWeb URL Management Management Agent Query AgentWeb Intelligence Web/ Search Engine Semantic Agent Protocols andSearching and Standards Web InternetRetrievalKnowledge Ontology Tools Core Social NetworkingManagement on Web Ontology Agent Services Agent CustomizedWeb Mining ServicesAgent BasedAgent BasedWebWeb Agent based webAcknowledgement 49 Created By Priti Srinivas Sajja
  50. 50. Intelligent Applications for WebArtificialIntelligence WebBio-inspired Local Databases Base Domain Databases BaseWeb Ontology Knowledge BaseWeb IntelligenceSearching and Query Manager Search Engine VisualizationRetrievalKnowledgeManagement on Web Web BowserWeb Mining Client Client ClientAgent BasedAgent BasedWebWeb Figure 11.10 Information retrieval agentAcknowledgement 50 Created By Priti Srinivas Sajja
  51. 51. Intelligent Applications for WebArtificialIntelligenceBio-inspired  Agent for semantic analysisWeb  Verification and validation (V&V) agent  Finding suitable web services agentWeb Intelligence  Crawler agentSearching andRetrieval  Explanation and reasoning AgentKnowledge  Natural language interface agentManagement on Web  Communication agentWeb Mining  Network traffic management agentAgent BasedAgent BasedWebWeb  Mobile agent for personalized contentAcknowledgement representation 51 Created By Priti Srinivas Sajja
  52. 52. Intelligent Applications for WebArtificialIntelligence Major References  Knowledge-based systems, Akerkar RA and Priti Srinivas Sajja, Jones & BartlettBio-inspired Publishers, Sudbury, MA, USA (2009)  Intelligent technologies for web applications”, Priti Srinivas Sajja, RajendraWeb Akerkar; CRC Press (Taylor & Francis Group), Boka Raton, FL, USA (2012)Web Intelligence Other References  llustrationsOf.comSearching and  coders-view.blogspot.comRetrieval  info.ideal.comKnowledge  http://businessintelligencetalk.blogspot.inManagement on Web   Engadget.comWeb Mining   lih.univ-lehavre.frAgent Based  business2press.comWeb  globalswarminghoneybees.blogspot.comAcknowledgement  52 Created By Priti Srinivas Sajja
  53. 53. Intelligent Applications for WebArtificialIntelligenceBio-inspiredWebWeb IntelligenceSearching andRetrievalKnowledgeManagement on Web To the participants and authority of the AICTE sponsored Staff Development Programme on Data Mining,Web Mining 16-28 April, 2012Agent Based at theWeb L. J. Institute of Engineering & Technology, Ahmedabad.Acknowledgement 53 Created By Priti Srinivas Sajja