Detalles del recurso

Descripción

The increasing use of mobile social networks has lately transformed news media. Real-world events are nowadays reported in social networks much faster than in traditional channels. As a result, the autonomous detection of events from networks like Twitter has gained lot of interest in both research and media groups. DBSCAN-like algorithms constitute a well-known clustering approach to retrospective event detection. However, scaling such algorithms to geographically large regions and temporarily long periods present two major shortcomings. First, detecting real-world events from the vast amount of tweets cannot be performed anymore in a single machine. Second, the tweeting activity varies a lot within these broad space-time regions limiting the use of global parameters. Against this background, we propose to scale DBSCAN-like event detection techniques by parallelizing and distributing them through a novel density-aware MapReduce scheme. The proposed scheme partitions tweet data as per its spatial and temporal features and tailors local DBSCAN parameters to local tweet densities. We implement the scheme in Apache Spark and evaluate its performance in a dataset composed of geo-located tweets in the Iberian peninsula during the course of several football matches. The results pointed out to the benefits of our proposal against other state-of-the-art techniques in terms of speed-up and detection accuracy.

Pertenece a

Digital.CSIC  

Autor(es)

Capdevila, Joan -  Pericacho, Gonzalo -  Torres, Jordi -  Cerquides, Jesus - 

Id.: 70423047

Idioma: eng  - 

Versión: 1.0

Estado: Final

Palabras claveEvent detection - 

Tipo de recurso: Capítulo de libro  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: closedAccess

Requerimientos técnicos:  Browser: Any - 

Relación: [References] MINECO/TIN2015-65316; MINECO/TIN2015-66863-C2-1-R
[References] Sí

Fecha de contribución: 19-dic-2017

Contacto:

Localización:
* doi: https://doi.org/10.1007/978-3-319-49583-5_27
* isbn: 978-3-319-49582-8

Otros recursos del mismo autor(es)

  1. Improving Max-Sum through Decimation to Solve Loopy Distributed Constraint Optimization Problems In the context of solving large distributed constraint optimization problems (DCOP), belief-propagat...
  2. Algorithms for graph-constrained coalition formation in the real world Coalition formation typically involves the coming together of multiple, heterogeneous, agents to ach...
  3. A graphical formalism for mixed multi-unit combinatorial auctions Mixed multi-unit combinatorial auctions are auctions that allow participants to bid for bundles of g...
  4. ALR n: accelerated higher-order logistic regression This paper introduces Accelerated Logistic Regression: a hybrid generative-discriminative approach t...
  5. Alleviating Naive Bayes attribute independence assumption by attribute weighting Despite the simplicity of the Naive Bayes classifier, it has continued to perform well against more ...

Otros recursos de la mismacolección

  1. Propiedades físicas de objetos transneptunianos y centauros Los objetos transneptunianos (TNOs) son aquellos cuerpos del Sistema Solar con órbitas cuyo semieje ...
  2. Probabilidad y Economía 4. Mercados financieros continuos 150 pags. ; 170 cm x 240 cm
  3. Improved Cloud resource allocation: how INDIGO-Datacloud is overcoming the current limitations in Cloud schedulers Trabajo presentado a: 22nd International Conference on Computing in High Energy and Nuclear Physics ...
  4. Exploring interacting topological insulators with ultracold atoms: The synthetic creutz-hubbard model 25 pags., 13 figs. -- Open Access funded by Creative Commons Atribution Licence 4.0
  5. Resource provisioning in Science Clouds: Requirements and challenges Early View (Online Version of Record published before inclusion in an issue): Version of record onli...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.