Towards BOTTARI: Using Stream Reasoning to Make Sense of Location-Based Micro-posts

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7117)


Consider an urban environment and its semi-public realms (e.g., shops, bars, visitors attractions, means of transportation). Who is the maven of a district? How fast and how broad can such maven influence the opinions of others? These are just few of the questions BOTTARI (our Location-based Social Media Analysis mobile app) is getting ready to answer. In this position paper, we recap our investigation on deductive and inductive stream reasoning for social media analysis, and we show how the results of this research form the underpinning of BOTTARI.


Data Stream Augmented Reality Sentiment Analysis Group Pattern Continuous Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.CEFRIEL – ICT InstitutePolitecnico of MilanoMilanoItaly
  2. 2.Dip. di Elettronica e dell’InformazionePolitecnico di MilanoMilanoItaly
  3. 3.Corporate TechnologySIEMENS AGMuenchenGermany
  4. 4.SaltluxSeoulKorea

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