<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Huge amount of information has become now available in web services due to their popularity. This web data contains user-contributed information for a variety of emergency events. However, tracking these emergency events is often limited by the lack of efficient tools to analyze the potential events or topics over time, since these events are inherently difficult to predict due to the interference of other unpredictable evolutions. In this paper we propose a two-phase approach, in which we first introduce a novel extraction algorithm to acquire relevant web data and then we utilize a limit theory to determine the periodical convergence time of a specific event, and an event tracking model is constructed using the extracted web data. Based on the significance of multiple features weights and clustering solutions, the interplay between the ordinary events and latent events is discovered to efficiently track the emergency events. Finally, we conduct extensive experiments to verify the effectiveness and efficiency of our approach.</p></abstract>

Huge amount of information has become now available in web services due to their popularity. This web data contains user-contributed information for a variety of emergency events. However, tracking these emergency events is often limited by the lack of efficient tools to analyze the potential events or topics over time, since these events are inherently difficult to predict due to the interference of other unpredictable evolutions. In this paper we propose a two-phase approach, in which we first introduce a novel extraction algorithm to acquire relevant web data and then we utilize a limit theory to determine the periodical convergence time of a specific event, and an event tracking model is constructed using the extracted web data. Based on the significance of multiple features weights and clustering solutions, the interplay between the ordinary events and latent events is discovered to efficiently track the emergency events. Finally, we conduct extensive experiments to verify the effectiveness and efficiency of our approach.

Efficient Emergency Event Tracking Using Features of Web Data

School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

Beijing BoWang China Science and Technology CO&LTD, Beijing 100053, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Efficient Emergency Event Tracking Using Features of Web Data"}

Huge amount of information has become now available in web services due to their popularity. This web data contains user-contributed information for a variety...

Efficient Emergency Event Tracking Using Features of Web Data

Publicado en línea: 05 oct 2015

Páginas: 68 - 87

DOI: https://doi.org/10.1515/cait-2015-0042

Palabras clave
Emergency event, event tracking, web data extraction, clustering

© Qunhui Wu et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Efficient Emergency Event Tracking Using Features of Web Data

Publicado en línea: 05 oct 2015

Páginas: 68 - 87

DOI: https://doi.org/10.1515/cait-2015-0042

Palabras claveEmergency event, event tracking, web data extraction, clustering

© Qunhui Wu et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Emergency event, event tracking, web data extraction, clustering