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Web Topics

Web Topics



Web, Open Social, Semantic Web, Web Mining, Cloud Computing

Web, Open Social, Semantic Web, Web Mining, Cloud Computing



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Web Topics Web Topics Presentation Transcript

  • World Wide Web Presented By Bharath Praveen Swathi
  • World Wide Web
    • The World Wide Web was created in 1989 by Tim Berners-Lee, working at the European Organization for Nuclear Research (CERN) in Geneva, Switzerland and released in 1992
    • Web - Accessing information over internet
    • It is not Internet – Network of networks
      • Email (SMTP), File sharing (FTP)
    • System of interlinked documents
    • Browser / Web Browser
    • The first Web browser, written by Tim Berners Lee and introduced in early 1991 ran on NeXT
  • Architecture
  • URI, URN & URL
    • < URI > := < scheme> : < scheme-specific-part >
    • Difference between URL, URN, and URI:
      • URL: http://www.tmrf.org/kpr/issue1.htm
      • URN: www.tmrf.org/kpr/issue1.htm#one
      • URI: http://www.tmrf.org/kpr/issue1.htm#one
  • Web Protocols
    • ARP: Address Resolution Protocol DHCP: Dynamic Host Configuration Protocol DNS: Domain Name Service DSN: Data Source Name FTP: File Transfer Protocol HTTP: Hypertext Transfer Protocol IMAP: Internet Message Access Protocol ICMP: Internet Control Message Protocol IDRP: ICMP Router-Discovery Protocol IP: Internet Protocol IRC: Internet Relay Chat Protocol POP3: Post Office Protocol version 3 PAR: Positive Acknowledgment and Retransmission RLOGIN: Remote Login SMTP: Simple Mail Transfer Protocol SSL: Secure Sockets Layer SSH: Secure Shell TCP: Transmission Control Protocol TELNET: TCP/IP Terminal Emulation Protocol UPD: User Datagram Protocol UPS: Uninterruptible Power Supply
  • HTTP Hyper Text Transfer Protocol
    • HTTP 1.1
      • Persistent connections
      • Pipelining
      • Cache validation commands
  • Request & Response
    • Request
      • GET
      • POST
  • Languages used
    • Client Side
      • HTML, CSS, Javascript, AJAX, Flex3
    • Server Side
      • .NET (Asp.net, VB.net, c#.net)
      • Java (JSP, Servlets, Plain java class)
      • CGI Perl / PHP
    • Other Languages
      • Ada 95, Applescript, BEF & Dylan (similar to PASCAL), CCI (Common Client Interface) , CMM, Guile, Hypertalk, Icon, KQML (Knowledge Query and Manipulation language), Linda, Lingo, Lisp, ML, Modula 3, Obliq, Phantom, Python, ReXX, ScriptX, SDI (Software Development Interface),VRML
  • Web 2.0
    • AJAX
      • Reverse AJAX
    • Democracy
      • (Wiki, reddit, digg, youtube)
    • RIA
    • SOA
    • Mashups
    • Widgets
    • Feeds, RSS, Web services
    • Blogging
    • Tagging
  • Ajax
    • Architecture
  • Ajax
    • Technologies Associated
      • XHTML & CSS for presentation
      • DOM to interact with data
      • XML & XSLT for interchange and manipulation of data
      • XMLHttpRequest object for asynchronous communication
      • Javascript to integrate all the above technologies
    • Advantages
      • Fast, No reload, updates the section of a page
    • Disadvantages
      • Actions are not registered with browser’s history
      • Need an alternate way to be indexed
      • JavaScript must be enabled on the browser
      • Server load
  • Reverse AJAX
    • Server pushes data to all alive clients
    • DWR Direct Web Remoting
  • Mashups
    • Mixing multiple service together to produce new
    • Types: Data & Enterprise mashups
    • Tools: Microsoft Popfly, Yahoo Pipes, Google Mashup editor
  • Widgets
    • UWA Universal Widget API from NetVibes
  • Feeds – RSS, JSON, Atom
  • Web 3.0
    • The Data Web
      • making data as openly accessible and linkable as Web pages
      • Querying for data across distributed RDF databases
    • Semantic web
  • Open Social
    • A common API for social applications across multiple websites
    • Supports interoperability with other social networks that support them
    • Core Services: People & Friends, Activities, Persistence
    • Platforms: google, hi5, myspace, Imeem
    • HTML, JavaScript, REST, OAUTH
  • Summary
    • Making the web more social
    • Current version 0.7
    • Orkut, MySpace, hi5, Netlog, Imeem, Linkedin
    • Easy to get data
    • Apache Shindig: to host open source applications
  • Semantic Web
    • Introduction
    • History
    • Architecture
    • Challenges
    • Future
    • Conclusion
    Logo of Semantic Web
  • What is Semantic Web ?
      • Meaningful representation of data on World Wide Web
      • Processed by humans as well as machines in global scale
  • Why do we need Semantic Web ?
    • Enhanced Search and Discovery
    • Enhanced System and Data Interoperability
    • Knowledge Management
    • Semantic Web Service
    • Electronic Commerce
  • History
    • 1989 – Vision of Tim-Berners Lee
    • 1994 – Presented at first WWW conference
    • 2002 – Architecture
  • Architecture Source: Lee, T. B. Semantic Web - XML2000 – Architecture. Retrieved July 11, 2008 from http://www.w3.org/2000/Talks/1206-xml2k-tbl/slide10-0.html
  • Unicode and URI
    • Unicode – International standard for encoding text
      • Ex: UTF-8, UTF-16
    • URI – Universal Resource Identifier
      • Uniform Resource Locator (URL)
        • Identify resources via a representation of their primary access mechanism
        • Ex: http://seal.ifi.unizh.ch
      • Universal Resource Name (URN)
        • Globally unique and persistent even when the resource ceases to exist or becomes unavailable.
        • Ex: urn:ISBN:0-395-36341-1
  • XML and Namespace
    • eXtensible Markup Language
      • Stores data in related entities
      • Provides standard for storage layout and logical structure
      • Supports syntactic interoperability
    • Namespace
      • Elements and attributes have expanded names
      • Expanded name = Namespace name + Local name
      • Namespace name – name holding URI
    • XML Schema
  • RDF – Resource Description Framework
    • Language for representing metadata of web resources
    • Framework for exchange of information between applications without loss of meaning
  • RDF Model
    • Resource - Thing being described by RDF expression
    • Property - Specific aspect, characteristic, attribute, or relation used to describe a resource.
    • Statement - A specific resource + a named property + the value of that property for that resource
      • Represented as 3-tuple – Subject, Predicate and Object
      • Ex: http://www.example.org/index.html has a creator called John Smith
  • RDF Model - Example Source: Manola, Miller, McBride (2004, February). The RDF Primer . W3C Recommendations.
  • RDF Model – Example (Contd…) Source: Manola, Miller, McBride (2004, February). The RDF Primer . W3C Recommendations.
  • Why RDF and not just XML ?
    • Many XML trees for single 3-tuple
    • XML parser cannot distinguish subject, object and property
    • RDF model – direct, unambiguous and decentralized
  • Why RDF and not just XML ? (Contd…)
    • Example
      • 3-tuple (index.html, John Smith, author)
      • Relationship: Index.html has author John Smith
      • <?xml version=&quot;1.0&quot;?>
      • <rdf:RDF xmlns:rdf=&quot;http://www.w3.org/1999/02/22-rdf- syntax-ns#&quot; xmlns:exterms=&quot;http://www.example.org/terms/&quot;>
      • <rdf:Description rdf:about=&quot;http://www.example.org/index.html&quot;> <exterms:creator>John Smith</exterms:creator> </rdf:Description>
      • </rdf:RDF>
  • Why RDF and not just XML ? (Contd…)
    • Possible XML trees
      • <author>
      • <uri> Index.html </uri>
      • <name>John Smith</name>
      • </author>
      • <document href=&quot; Index.html &quot;>
      • <author> John Smith </author>
      • </document>
      • <document> <details>
      • <uri>href=&quot; Index.html &quot;</uri>
      • <author> <name> John Smith </name> </author>
      • </details> </document> or maybe
      • <document>
      • <author>
      • <uri>href=&quot; Index.html &quot;</uri>
      • <details> <name> John Smith </name> </details>
      • </author>
      • </document>
  • RDF Schema (RDFS)
    • Collection of classes authored for specific purpose or domain
    • Classes organized in hierarchy
    • Describes inheritance hierarchies, class schemas, properties, domain and range and restriction for properties
    • Supports extensibility and reusability
    • Multiple views of same metadata
  • RDFS - Example
    • <Class ID=“Animal”>
    • <Class ID=&quot;Male&quot;>
    • <subclass Ofresource=&quot;#Animal&quot;/>
    • </Class> 
    • <Class ID=&quot;Female&quot;>
    • <subclass Ofresource=&quot;#Animal&quot;/>
    • <disjointFrom resource=&quot;#Male&quot;/>
    • </Class>
  • Web Ontology Language (OWL)
    • Extends from RDFS
    • Specifies axioms based on the classes of entities, their properties and relationships
    • Draw inference based on axioms
  • OWL (Contd…) Source: Lee, T. B. Semantic Web Road map. Retrieved July 11, 2008 from http://www.w3.org/DesignIssues/Semantic.html
  • Challenges
    • Standardizing Semantic Web Stack
    • Developing Ontologies
    • Converting existing WWW into Semantic Web
    • Capturing Cultural Semantics
    • Interoperability Issues
  • Some News…
    • SPARQL Protocol
    • Semantic Search Engines – Google, Yahoo, Intelliseek
    • Jena Semantic Web Toolkit – HP
    • Joseki Web API – HP
    • Wilbur – Nokia
  • What is Cloud Computing?
    • An emerging computing paradigm where data and services reside in massively scalable data centers and can be ubiquitously accessed from any connected devices over the internet.
    4+ billion phones by 2010 [Source: Nokia] Web 2.0-enabled PCs, TVs, etc.
  • Characteristics of Cloud Computing
    • Virtual – Physical location and underlying infrastructure details are transparent to users
    • Scalable – Able to break complex workloads into pieces to be served across an incrementally expandable infrastructure
    • Efficient – Services Oriented Architecture for dynamic provisioning of shared compute resources
    • Flexible – Can serve a variety of workload types – both consumer and commercial
  • Cloud Computing Building Blocks A massively scalable and flexible computing platform of the future, built on IBM and open source software, for hosting Web 2.0 and SOA applications. Business Benefits
    • Cost efficient model for creating
    • and acquiring information services
    • Removes or reduces IT management complexity
    • Increases business responsiveness with real-time capacity reallocation
    • Powers rich internet applications
    Enabling Technologies
    • Open source Linux platform
    • Xen open source systems virtualization
    • Automated provisioning of computing resources by Tivoli Provisioning Manager
    • Systems management and monitoring by IBM Tivoli Monitoring
    • Parallel computing clusters using Apache Hadoop
    • Open source Eclipse-based development tools for parallel applications
  • Cloud Computing Architecture IBM Monitoring v.6 DB2 Provisioning Management Stack Provisioning Manager v.5.1 WebSphere Application Server Monitoring Provisioning Baremetal & Xen VM Open Source Linux with Xen Tivoli Monitoring Agent Virtualized Infrastructure based on Open Source Linux & Xen Virtual Machine Virtual Machine Virtual Machine Virtual Machine Data Center – System x Apache
  • Examples of Cloud Computing Workloads
    • Web 2.0 applications
      • Software to scan voluminous Wikipedia edits to identify spam
      • Organize global news articles by geographic location
    • Data-intensive workloads based on scalable architectures.
    • Next generation rich media, such as virtual worlds, streaming videos, etc.
      • New services can be created and published via a completely integrated Eclipse-based environment
  • Joint IBM Google Announcement IBM Almaden Research Universities participating in initial pilot
    • Train future workforce with next generation computing skills
    • University initiative to promote open standards and emerging parallel computing model
    • Jointly provide compute platform of the future including hardware, software, and services to support new parallel computing curricula
    • Three active “clouds”
    Google U. Of Washington
  • Web Mining
    • Web mining is the use of data mining techniques to automatically discover and extract information from Web documents/services
  • Why is Web Information Retrieval Important?
    • Research
    • Health/Medicine
    • Travel
    • Business
    • Entertainment
    • Arts
  • Why is Web Information Retrieval Difficult?
    • The Abundance Problem
    • Hundreds of irrelevant documents returned in response to a search
    • query.
    • Limited Coverage of the Web
    • Largest crawlers cover less than 18% of Web pages
    • The Web is extremely dynamic
    • 􀂄 Lots of pages added, removed and changed every day
    • 􀂄 Very high dimensionality (thousands of dimensions)
    • 􀂄 Limited query interface based on keyword-oriented search
    • 􀂄 Limited customization to individual users
  • Web Mining Taxonomy Web Mining Web Usage Mining Web Structure Mining Web Content Mining
  • Web Mining Taxonomy
    • Web content mining : focuses on techniques for assisting a user in finding documents that meet a certain criterion (text mining)
    • Web structure mining : aims at developing techniques to take advantage of the collective judgment of web page quality which is available in the form of hyperlinks
    • Web usage mining : focuses on techniques to study the user behavior when navigating the web (also known as Web log mining and clickstream analysis)
  • Web Content Mining
    • Can be thought of as extending the work performed by basic search engines.
    • Search engines have crawlers to search the web and gather information, indexing techniques to store the information, and query processing support to provide information to the users
    • Web Content Mining is: the process of extracting knowledge from web contents
  • Semi-Structured Data
    • Content is, in general, semi-structured
    • Example:
    • 􀂄 Title
    • 􀂄 Author
    • 􀂄 Publication_Date Structured attribute/value pairs
    • 􀂄 Length
    • 􀂄 Category
    • 􀂄 Abstract Unstructured
    • 􀂄 Content
  • Text Mining
    • Document classification
    • Document clustering
    • Key-word based association rules
  • Web Structure Mining
    • Early days: keyword based searches
    • Keywords: “web mining”
    • Retrieves documents with “web” and mining”
    • Later on: cope with
    • 􀂄 Synonymy problem
    • 􀂄 Polysemy problem
    • 􀂄 stop words
    • Modern search engines use link structure as
    • important source of information
  • Central Question: Which useful information can be derived from the link structure of the web?
  • Some Answers
    • 1. Structure of Internet
    • 2. Google
    • 3. HITS: Hubs and Authorities
  • General Structure of the Web
  • Google
    • Search engine that uses link structure to calculate a quality ranking (PageRank) for each page
    • Intuition: PageRank can be seen as the probability that a “random surfer” visits a page
    • Keywords CMPE272 entered by user
    • Select pages containing CMPE272 and pages which have in-links with caption CMPE272 .
    • Font sizes of words in text: Words in larger or bolder font are assigned higher weights.
  • HITS (hyperlink-Induced Topic Search)
    • HITS uses hyperlink structure to identify authoritative Web sources for broad-topic information discovery
    • Premise: Sufficiently broad topics contain communities consisting of two types of hyperlinked pages:
    • 􀂄 Authorities: highly-referenced pages on a topic
    • 􀂄 Hubs: pages that “point” to authorities
    • A good authority is pointed to by many good hubs; a good hub points to many good authorities
  • Hubs and Authorities Hub pages point to interesting links to authorities = relevant pages Authorities are targets of hub pages
  • Web Usage Mining
    • Pages contain information
    • Links are “roads”
    • How do people navigate over the Internet?
    • ⇒ Web usage mining (Clickstream Analysis)
    • Information on navigation paths are logged.
  • Web Usage Analysis
  • Data Sources
  • Web Usage Mining Process
  • Data Preparation
    • Data cleaning
    • 􀂄 By checking the suffix of the URL name, for example, all log
    • entries with filename suffixes such as, gif, jpeg, etc
    • User identification
    • 􀂄 If a page is requested that is not directly linked to the
    • previous pages, multiple users are assumed to exist on the
    • same machine
    • 􀂄 Other heuristics involve using a combination of IP address,
    • machine name, browser agent, and temporal information to
    • identify users
    • Transaction identification
    • 􀂄 All of the page references made by a user during a single visit
    • to a site
    • 􀂄 Size of a transaction can range from a single page reference to
    • all of the page references
  • References - Web
    • Bryan Basham, Kathy Sierra, & Bert Bates. (2008). Head first servlets and JSP Oreilly & Associates Inc.
    • Dan Harkey, Robert Orfali, & Jeri Edwards. Client/Server survival guide (Third ed.) Wiley.
    • Open social. (2008). http://www.opensocial.org/
    • Praveen, A. (2008). Job quest mashup.http://praveen.987mb.com/Projects/JobDashBoard/HTML/JobQuest.html
    • Wikipedia. (2008). http://en.wikipedia.org/wiki/Main_Page
  • References – Semantic Web
    • Lee, T. B. (1998, September). Semantic Web Road map. Retrieved July 11, 2008 from http://www.w3.org/DesignIssues/Semantic.html
    • Lee, T. B. Semantic Web - XML2000. Retrieved July 11, 2008 from http://www.w3.org/2000/Talks/1206-xml2k-tbl/Overview.html
    • Manola, Miller, McBride (2004, February). The RDF Primer . W3C Recommendations.
    • Lee, T. B. (1998, September). Why RDF model is different from the XML model. Retrieved July 11, 2008 from http://www.w3.org/DesignIssues/RDF-XML.html
    • W3C. (1999, January). Resource Description Framework (RDF) Model and Syntax Specification. Retrieved July 11, 2008 from http://www.w3.org/TR/PR-rdf-syntax/
    • Palmer, S., B. (1999, September). The Semantic Web: An introduction. Retrieved July 11, 2008 from http://infomesh.net/2001/swintro/#itWorks
  • References
    • www.umass.edu/research/rld/iln/uploads/ Cloud %20 Computing %20Oct%2003%20Ext. ppt
    • en.wikipedia.org/wiki/ Cloud _ computing
    • infolab.stanford.edu/~ullman/ mining /2006/lectureslides/ web %20 mining %20overview.pdf
    • www.cs.uic.edu/~liub/ Web Content Mining .html
    • en.wikipedia.org/wiki/ Web _ mining