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Issue No.03 - July-Sept. (2012 vol.19)
pp: 81-87
Tat-Seng Chua , National University of Singapore
Huanbo Luan , National University of Singapore and Tsinghua University
Maosong Sun , Tsinghua University
Shiqiang Yang , Tsinghua University
ABSTRACT
The Web has revolutionized the way we create, disseminate, and consume information. Users have changed from passive recipients of information to active content consumers and creators, and the nature of information has also changed from static text to dynamic multimedia. With the widespread use of social networks, live user-generated content (UGC) has begun to dominate the Internet. Such UGC covers a range of media, from text (tweets, forums, and Facebook messages) to images (Instagram and Flickr), videos (YouTube), location check-ins (Foursquare), and community question-and-answer forums (Yahoo!Answers and WikiAnswers). The NUS-Tsinghua Center for Extreme Search (or NExT Center) is collaboration between the National University of Singapore (NUS) and Tsinghua University that focuses on the novel, challenging task of analyzing and organizing UGC to make it available for general access. This article summarizes the authors' initial work on live monitoring of raw UGC and events as they unfold. It highlights six of the research projects carried out by the NExT Center along the lines of crawling, analyzing, and visualizing live UGC data.
INDEX TERMS
Web sites, Web services, Search methods, Search engines, Mobile communication, Social network services, Content based retrieval, mobile applications, multimedia, social networking, NExT Center, NUS-Tsinghua Center for Extreme Search, user-generated content, data analytics, extreme search, geolocation
CITATION
Tat-Seng Chua, Huanbo Luan, Maosong Sun, Shiqiang Yang, "NExT: NUS-Tsinghua Center for Extreme Search of User-Generated Content", IEEE MultiMedia, vol.19, no. 3, pp. 81-87, July-Sept. 2012, doi:10.1109/MMUL.2012.39
REFERENCES
1. A. Saha et al., "Learning Evolving and Emerging Topics in Social Media: A Dynamic NMF Approach with Temporal Regularization," Proc. 5th ACM Int'l Conf. Web Search and Data Mining (WSDM), 2012, pp. 693–702.
2. A. Sadilek et al., "Finding Your Friends and Following Them to Where You Are," Proc. 5th ACM Int'l Conf. Web Search and Data Mining (WSDM), ACM Press, 2012, pp. 723–732.
3. Z. Ming et al., "Prototype Hierarchy Based Clustering for the Categorization and Navigation of Web Collections," Proc. 33rd Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, ACM Press, 2010, pp. 2–9.
4. L. Tang et al., Community Detection and Mining in Social Media, Morgan & Claypool Publishers, 2010.
5. H. Liu et al., "Robust Clustering as Ensemble of Affinity Relations," Proc. 24th Ann. Conf. Neural Information Processing Systems (NIPS), Curran Associates Publisher, 2010, pp. 1414–1422.
6. S. Liu et al., "Street-to-Shop: Cross-Scenario Clothing Retrieval Via Parts Alignment and Auxiliary Set," Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition (CVPR), IEEE CS Press, 2012.
7. G. Li et al., "Desks: Direction-Aware Spatial Keyword Search," Proc. Int'l Conf. Data Eng. (ICDE), IEEE CS Press, 2012, pp. 459–470.
8. L. Shi et al., "S3: An Efficient Shared Scan Scheduler on MapReduce Framework," Proc. 2011 Int'l Conf. Parallel Processing (ICPP), IEEE CS Press, 2011, pp. 325–334.
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