otm:the_matching_of_causal_sequences

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 and Paul. Toti follows Dacha and Dacha follows Paul; Kuky follows nobody and nobody follows Kuky. The figure shows that Paul publishes a tweet and Dacha receives this tweet and retweets it. The figure also shows that Kuky publishes a tweet and nobody receives it. Based on the popularity measurement, the popularity of Paul's tweet is 1 and that of Kuky is 0. Although Kuky and Paul. Toti follows Dacha and Dacha follows Paul; Kuky follows nobody and nobody follows Kuky. The figure shows that Paul publishes a tweet and Dacha receives this tweet and retweets it. The figure also shows that Kuky publishes a tweet and nobody receives it. Based on the popularity measurement, the popularity of Paul's tweet is 1 and that of Kuky is 0. Although Kuky
 and Paul would have published the same tweet, the popularity of Kuky's tweet is 0 because his tweet did not cause any retweet. An analysis based on the distributed causal relations observed between tweets and retweets can determine how many users retweet a given tweet. For example, Paul's tweet caused Dacha's retweet. and Paul would have published the same tweet, the popularity of Kuky's tweet is 0 because his tweet did not cause any retweet. An analysis based on the distributed causal relations observed between tweets and retweets can determine how many users retweet a given tweet. For example, Paul's tweet caused Dacha's retweet.
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 {{ :otm:twitter.png?390x333 |}} {{ :otm:twitter.png?390x333 |}}
  
-As OTM supports the definition of entities that observe and react to a Web application execution trace, we have extended our prototype to observe and react to distributed execution traces using the vector clock algorithm.  +Using WeCa, which combines [[citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.2931|stateful aspects]] and [[en.wikipedia.org/wiki/Vector_clock|vector clocks]], we show how to react to distributed causal relations to determine the tweet popularity:
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-Social Web applications like [[http://twitter.com/|Twitter]] or [[reader.google.com|Google Reader]], where users send or receive messages from other users, have been identified as good examples to use the vector clock algorithm in order to analyze the flow information of these social Web applications(([[http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5562765|Using Vector Clocks to Visualize Communication Flow]].)). Using OTM, we show how the matching distributed execution traces can analyze popularity of a piece of news in a social Web applications like Twitter: +
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-  * [[otm/the_matching_of_causal_sequences/example1|Popularity of a piece of news]]+
  
 +  * [[otm/the_matching_of_causal_sequences/example1|Tweet Popularity]]
    
-Go [[|OTM home]].+Go [[../weca|WeCa home]].
    
  
  
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