Difference between revisions of "Linked Open Social Signals"

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== Scalability ==
 
== Scalability ==
* 4store Amazon Machine Image and Billion Triple Challenge Data Set[http://thinklinks.wordpress.com/2009/10/27/4store-amazon-machine-image-and-billion-triple-challenge-data-set/]
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* 4store Amazon Machine Image and Billion Triple Challenge Data Set http://thinklinks.wordpress.com/2009/10/27/4store-amazon-machine-image-and-billion-triple-challenge-data-set/

Revision as of 02:03, 24 March 2010

We explore the symbiosis of real time analysis and linked data. Representing social signals as structured data will enable flexibility in handling the information overload of those interested in collectively analyzing social signals for sensemaking.

This is work in progress by Pablo N. Mendes (Kno.e.sis), Alex Passant (DERI), Pavan Kapanipathi (Kno.e.sis) and Dr. Amit P. Sheth (Kno.e.sis). It builds upon Twitris and SMOB.

Quick Info

  • Real Time: the load estimate for the health care topic drinking from the firehose is
    • 1 post per second
    • 35K triples per hour (tph) or 10 triples per second, steady over HTTP SPARQL Update. Feasible?
  • Writeup: Get it from here.

Pitching

Some motivation: decentralized semantic microblogging.

  • Information Overload
    • As reported by ReadWriteWeb recently, during an emergency it’s practically impossible to get status updates on things like roads, hospitals, airports, and people using Twitter [1]
    • Twitter as a poor vehicle for marketing [2]. Many people make up hashtags as they tweet, exploding the semantic graph, creating more semantic dispersion. Some promising new tools that can help you quickly put a hashtag in context — or let people easily look up the meaning of the hashtags you launch or use [3]
    • Wouldn’t it be cool if Twitter had a topic backbone and you could snap your tweets to it as you write them? [4]
  • Push vs Pull
    • Siegel’s rule for information life span: The half-life relevance of a piece of pushed information is about the same as the frequency of the medium. [5]
      • Twitter developed a new set of frameworks @anywhere for adding this Twitter experience anywhere on the web. Imagine being able to follow a New York Times journalist directly from her byline, tweet about a video without leaving YouTube, and discover new Twitter accounts while visiting the Yahoo! home page—and that’s just the beginning. [6]
    • Persistent Search: http://billburnham.blogs.com/burnhamsbeat/2006/04/persistent_sear.html
    • Understanding the Real-Time Web for Web Developers [7]

Related

  • Bibliography of Research on Twitter & Microblogging [8]
  • Priamos a middleware architecture for real time semantic web [9]

At Kno.e.sis

  • Social Signals @kno.e.sis
  • A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, February 17, 2009.
  • A. Sheth, Citizen Sensing, Social Signals, and Enriching Human Experience- IEEE Internet Computing, July/August 2009.
  • Meenakshi Nagarajan, Karthik Gomadam, Amit P. Sheth, Ajith Ranabahu, Raghava Mutharaju, Ashutosh Jadhav: Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences. WISE 2009: 539-553 [10]

At DERI

Semantic Microblogging

  • http://semantictwitter.appspot.com/
    • HyperTwitter is semantic hashtags on Twitter. Associate hashtags together and then performer searches. Clever. Though you might want to create a special Twitter account for doing the associations rather than sending these commands through your main Twitter account.
    • Technical Report

Maybe related

  • Short and Tweet: Experiments on Recommending Content from Information Streams [11]
  • Cheng. Fall'09 class project at iSchool (Berkeley). Classifying Metatweets pdf
  • Klout on health care: [12]
  • Topsy on health care: [13]
  • Krishnamurty, Gill, Arlitt. SIGCOMM'08. A few chirps about twitter. pdf
    • classifies 100,000 users in broadcasters, acquaintances, miscreants or evangelists.

Streaming SPARQL

  • C-SPARQL: SPARQL for Continuous Querying WWW'09
  • Streaming SPARQL - Extending SPARQL to Process Data Streams ESWC'08
  • A SPARQL Engine for Streaming RDF Data SITIS'07

Scalability