Skip to main content

Towards BOTTARI: Using stream reasoning to make sense of location-based micro-posts

13Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.

This artice is free to access.

Abstract

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. © 2012 Springer-Verlag.

Cite

CITATION STYLE

APA

Celino, I., Dell’Aglio, D., Della Valle, E., Huang, Y., Lee, T., Kim, S. H., & Tresp, V. (2012). Towards BOTTARI: Using stream reasoning to make sense of location-based micro-posts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7117 LNCS, pp. 80–87). https://doi.org/10.1007/978-3-642-25953-1_7

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free