Emergency Event Web Information Acquisition using Crowd Web Sensors

In the domain of emergent event analysis, it is still a difficult issue to acquire the event information from the Web efficiently. To solve the problem, this paper proposes a crowdsensing-based Web crawler for emergent event analysis. When an emergent event occurs, some web users post event information on the Web with geographical position. These web users can be regarded as crowd sensors. In the proposed method, the crawler takes advantage of the information from these crowd sensors, such as semantic information, geographical information, sentiment information, etc., to get the information of event efficiently. Experimental results show that the proposed method can improve the efficiency of crawlers when compared with universal crawlers both in the period of sparse information and in the period of eruptible information.

[1]  Arnon Rungsawang,et al.  Learnable topic-specific web crawler , 2002, J. Netw. Comput. Appl..

[2]  Elizabeth Chang,et al.  A Transport Service Ontology-based Focused Crawler , 2008, 2008 Fourth International Conference on Semantics, Knowledge and Grid.

[3]  Jun Zhang,et al.  Online Comment-Based Hotel Quality Automatic Assessment Using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map , 2015, IEEE Transactions on Fuzzy Systems.

[4]  Arputharaj Kannan,et al.  LSCrawler: A Framework for an Enhanced Focused Web Crawler Based on Link Semantics , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[5]  Daniel Dajun Zeng,et al.  ExNa: an efficient search pattern for semantic search engines , 2016, Concurr. Comput. Pract. Exp..

[6]  Yoelle Maarek,et al.  The Shark-Search Algorithm. An Application: Tailored Web Site Mapping , 1998, Comput. Networks.

[7]  Reinier Post,et al.  Information Retrieval in the World-Wide Web: Making Client-Based Searching Feasible , 1994, Comput. Networks ISDN Syst..

[8]  George D. Haddow,et al.  Introduction to Emergency Management , 2003 .

[9]  Sebastiano Vigna,et al.  UbiCrawler: a scalable fully distributed Web crawler , 2004, Softw. Pract. Exp..

[10]  G. Aghila,et al.  Ontology-based Web crawler , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

[11]  Sharma Shruti,et al.  A Novel Architecture of a Parallel Web Crawler , 2011 .

[12]  Joel J. P. C. Rodrigues,et al.  Exploring Social Networks and Improving Hypertext Results for Cloud Solutions , 2016, Mob. Networks Appl..

[13]  Torsten Suel,et al.  Design and implementation of a high-performance distributed Web crawler , 2002, Proceedings 18th International Conference on Data Engineering.

[14]  Marc Najork,et al.  Mercator: A scalable, extensible Web crawler , 1999, World Wide Web.

[15]  Mohamed Magdy Gharib Farag,et al.  Intelligent Event Focused Crawling , 2016 .

[16]  Sangaralingam Kajanan,et al.  A Mobile App Search Engine , 2013, Mob. Networks Appl..

[17]  Yunhuai Liu,et al.  Crowdsourcing based social media data analysis of urban emergency events , 2017, Multimedia Tools and Applications.

[18]  Shunxiang Zhang,et al.  Mining temporal explicit and implicit semantic relations between entities using web search engines , 2014, Future Gener. Comput. Syst..

[19]  Mukesh Singhal,et al.  A cloud-based web crawler architecture , 2015, 2015 18th International Conference on Intelligence in Next Generation Networks.

[20]  Jun Zhang,et al.  A multi-level text representation model within background knowledge based on human cognitive process for big data analysis , 2013, 2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing.

[21]  Sybille Peters,et al.  A New Approach Towards Vertical Search Engines - Intelligent Focused Crawling and Multilingual Semantic Techniques , 2010, WEBIST.

[22]  Lan Chen,et al.  Knowle: A semantic link network based system for organizing large scale online news events , 2015, Future Gener. Comput. Syst..