2017년 츈계 학술대회
Current Result Document :
| 한글제목(Korean Title) |
소셜미디어 수집과 분석을 위한재난 빅 데이터 플랫폼의 설계 |
| 영문제목(English Title) |
Design of a Disaster Big Data Platform for Collecting and Analyzing Social Media |
| 저자(Author) |
반퀴엣뉘엔
신응억뉘엔
양쯔엉뉘엔
김경백
Van-Quyet Nguyen
Sinh-Ngoc Nguyen
Giang-Truong Nguyen
Kyungbaek Kim
|
| 원문수록처(Citation) |
VOL 24 NO. 01 PP. 0661 ~ 0664 (2017. 04) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Recently, during disasters occurrence, dealing with emergencies has been handled well by the early transmission of disaster relating notifications on social media networks (e.g., Twitter or Facebook). Intuitively, with their characteristics (e.g., real-time, mobility) and big communities whose users could be regarded as volunteers, social networks are proved to be a crucial role for disasters response. However, the amount of data transmitted during disasters is an obstacle for filtering informative messages; because the messages are diversity, large and very noise. This large volume of data could be seen as Social Big Data (SBD). In this paper, we proposed a big data platform for collecting and analyzing disaster' data from SBD. Firstly, we designed a collecting module; which could rapidly extract disaster' information from the Twitter; by big data frameworks supporting streaming data on distributed system; such as Kafka and Spark. Secondly, we developed an analyzing module which learned from SBD to distinguish the useful information from the irrelevant one. Finally, we also designed a real-time visualization on the web interface for displaying the results of analysis phase. To show the viability of our platform, we conducted experiments of the collecting and analyzing phases in 10 days for both real-time and historical tweets, which were about disasters happened in South Korea. The results prove that our big data platform could be applied to disaster information based systems, by providing a huge relevant data; which can be used for inferring affected regions and victims in disaster situations, from 21.000 collected tweets.
|
| 키워드(Keyword) |
|
| 파일첨부 |
PDF 다운로드
|
