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Networking and connectedness a few disjointed thoughts - r. ramachandran


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  • 1. Networking and Connectedness - a few disjointed thoughts By R. Ramachandran National Technical Research Organisation
  • 2. Web 1.0 vs Web 2.0 Web 1.0 Web 2.0 Server oriented (Provider generate content) User oriented (User can generate content without having a server) User passive (User “consumes” content ) User active (User generates content) Closed Applications Open Applications (Applications can be combined - MashUps)
  • 3. Web Evolution ENCLYCOPEDIA Britannica Video Buster You Tube Picassa Web Photo WIKIPEDIA The Free Enclyclopedia Hard Copies and Prints Albums and offline media Book Racks Past Web 1.0 Web 2.0 flickr
  • 4. Crowdsourcing: Volunteered Geographic Information
    • Crowdsourcing: Outsourcing of work not to a contractor but to anyone(to the crowd) in the internet
    • Volunteered Geographic Information: Revolution in Mapping by way of provision of tools to create,assemble, and disseminate geographic data provided voluntarily by individuals (Goodchild, 2007)
    • Examples:
      • Wikimapia
      • Openstreetmap
      • Google My Maps.
  • 5. Wikimapia & Openstreetmap
    • WikiMapia is a privately owned, online map and satellite imaging resource that combines Google Maps with a wiki system, allowing users to add information, in the form of a note, to any location on Earth.
    • Website was launched on may 2006. Its aim is to "describe the whole world". It now has over 14,600,000 places marked.
    • Openstreetmap is a free project open to everyone to collect spatial data was founded in 2004 and operational since 2006. GPS Receiver imported data is the most important data source. Local knowledge also helps to add value to the spatial data.
  • 6. DELHI – Openstreetmap with High Resolution Satellite Imagery
  • 7. WikiMapia – Entries Growth
  • 8. Openstreetmap
  • 9.  
  • 10. Crowd Sourcing - USGS
    • Disaster Response:
      • The USGS is trying to achieve a denser and more uniform spacing of seismographs in select urban areas to provide better measurements of ground motion during earthquakes.
      • The seismographs connect to a local network via WiFi and use existing broadband connections to transmit data after an earthquake.
      • The instruments are designed to be installed in private homes, businesses, public buildings and schools with an existing broadband connection to the internet.
      • USGS enables reporting by internet users of disaster events as felt in their locality with description in a web mapping application
  • 11. USGS: Online reporting of events Geocoding
  • 12. USGS: Online reporting of events
  • 13. Online Tools for Rescue
    • Online Tools came to rescue after Haiti Earthquake and Japanese earthquakes
    • Google’s People Finder
      • As of 5am Pacific Time, People Finder was getting a couple of hundred new updates every 10 minutes or so and climbing fast from 4500 records.
    • Ushahidi’s Crisis Map
      • Anyone on the ground can text in the location of a trapped person, and these locations are then collected on a map. One can also text in where to find aid, a pop-up hospital or a precarious building that should be avoided.
  • 14. Ushahidi – Mapping In Haiti Rescue
  • 16. “ TRUE” NETWORKING Data Layers Database Planning & Acquisition Data Analysis Information System Monitoring Systems Value = Usefulness Cost
  • 17. High Productivity
  • 18. Sarnoff’s Law
    • Law: For a broadcast network usefulness is directly proportional to number of nodes
    • If ‘n’ clients exists
    Value = n Cost
  • 19. Metcalfe’s Law
    • Law: For a ‘ fully’ connected network usefulness is O(n 2 )
    • Example : Telephone Network
    No. of Nodes Cost Benefit Value = n 2 Cost
  • 20. “ Information Life Cycle” 0 T 0 T Relevance of Information Actual Function – In Reality Value = n 2 Cost * Info. life Time
  • 21. Reed’s Law
    • Law: For a group of highly interacting nodes usefulness is O(2 n )
    Value = 2 n Cost
  • 22.
    • Reed's law is the assertion of David P. Reed that the utility of large networks, particularly social networks, can scale exponentially with the size of the network.
    • The reason for this is that the number of possible sub-groups of network participants is 2 N  −  N  − 1, where N is the number of participants. This grows much more rapidly than either
      • the number of participants, N , or
      • the number of possible pair connections, N ( N  − 1)/2 (which follows Metcalfe's law).
    Reed’s Law
  • 23. Reed’s Law
    • The true value of a network rests not in its nodes, but in its connections.
    • According to Reed, it’s the connections that add more value and than the nodes.
    • If you want to empower your base and maximize the power of networks, then you should help your nodes make connections wherever possible
  • 24. Reed’s Law
  • 25. Semantics in data analysis
    • Ask complex questions to a rich database and find answers quickly.
    • Today many semantics are built through tags or metadata.
  • 26. Semantics in data analysis
    • Effective implementation of semantic web involves evolution of many related technologies.
      • Ontology tools
      • Data interchange formats
      • Resource Description Framework
    • Formal description of terms , concepts and relationships in a given knowledge domain.
  • 27. Semantics in data analysis
    • Universalisation of semantic web is a very desirable objective.
    • Among specialized communities and organisation , it is an achievable objective.
    • These are the potential areas for big success in near future.
  • 28. Thank You