Publication statistics

Pub. period:2003-2011
Pub. count:8
Number of co-authors:18


Number of publications with 3 favourite co-authors:

Yimin Wei:
Yuzhong Qu:
Guilin Qi:



Productive colleagues

Gang Wu's 3 most productive colleagues in number of publications:

Edward Y. Chang:26
Juanzi Li:9
Yuzhong Qu:8

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Gang Wu


Publications by Gang Wu (bibliography)

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Yang, Mengdong and Wu, Gang (2011): Caching intermediate result of SPARQL queries. In: Proceedings of the 2011 International Conference on the World Wide Web 2011. pp. 159-160.

The complexity and growing scale of RDF data has made data management back end the performance bottleneck of Semantic Web applications. Caching is one of the ways that could solve this problem. However, few existing research projects focus on caching in RDF data processing. We present an adaptive caching scheme that caches intermediate result of basic graph pattern SPARQL queries. Benchmark test results are provided to illustrate the effectiveness of our caching scheme.

© All rights reserved Yang and Wu and/or ACM Press

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Chen, Chuanfu, Wang, Ping, Liu, Yaqi, Wu, Dan, Wu, Gang and Ma, Haoqin (2011): The attitude of LIS chairs toward the iSchools movement in China: a contemporary grounded theory analysis. In: Proceedings of the 2011 iConference 2011. pp. 217-224.

The goal of this paper is to study the attitude of the deans and the department chairs of Library and Information Science (LIS) programs in Chinese universities toward the iSchools movement. We collected data from 36 deans and chairs using an open questionnaire and utilized grounded theory to analyze the results. Our study shows that most deans and chairs approve of the iSchool values of interdisciplinary study, information technology, and leadership, but they also expressed uncertainty regarding the future of iSchools. For the process of adopting the values of iSchools and joining the iSchools movement, the main obstacles to progress come from within the LIS discipline. The consensus among the deans and chairs is that the LIS education should reserve its traditional core values, at the same time it also should adopt iSchools values and expand into a wider area in the information profession.

© All rights reserved Chen et al. and/or ACM Press

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Wu, Gang, Yang, Mengdong, Wu, Ke, Qi, Guilin and Qu, Yuzhong (2010): Falconer: once SIOC meets semantic search engine. In: Proceedings of the 2010 International Conference on the World Wide Web 2010. pp. 1317-1320.

Falconer is a semantic Web search engine enhanced SIOC (Semantically-Interlinked Online Communities) application, which is designed to demonstrate the ability of accelerating the creation and reuse process of semantic Web data with easy-to-use user interfaces. In this process, semantic Web search engines feed existing semantic data into the SIOC framework, where new semantic data are composed by the community and indexed again by those search engines. Compared to existing social (semantic) Web applications, Falconer inherently conforms to SIOC specification. It provides semantic search engine based user registration suggestion, friends auto-discovery, and semantic annotation for forum post content. Another distinctive feature is that it enables users to subscribe any resource having a URI as the topic they are interested in. The relationships among users, topics, and posts are further visualized for analyzing the topic trends in the community. As all semantic data are formatted in RDF and RDFa, they can be queried with SPARQL query language.

© All rights reserved Wu et al. and/or their publisher

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Wu, Gang and Wei, Yimin (2010): Arnoldi versus GMRES for computing PageRank: A theoretical contribution to google's PageRank problem. In ACM Transactions on Information Systems, 28 (3) p. 11.

PageRank is one of the most important ranking techniques used in today's search engines. A recent very interesting research track focuses on exploiting efficient numerical methods to speed up the computation of PageRank, among which the Arnoldi-type algorithm and the GMRES algorithm are competitive candidates. In essence, the former deals with the PageRank problem from an eigen problem, while the latter from a linear system, point of view. However, there is little known about the relations between the two approaches for PageRank. In this article, we focus on a theoretical and numerical comparison of the two approaches. Numerical experiments illustrate the effectiveness of our theoretical results.

© All rights reserved Wu and Wei and/or ACM Press

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Wu, Gang, Li, Juanzi and Wang, Kehong (2008): System II: a hypergraph based native rdf repository. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 1035-1036.

To manage the increasing amount of RDF data, an RDF repository should provide not only necessary scalability and efficiency, but also sufficient inference capabilities. In this paper, we propose a native RDF repository, System, to pursue a better tradeoff among the above requirements. System takes the hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. In addition, a set of efficient semantic query processing techniques are designed. The results of performance evaluation on the LUBM benchmark show that System has a better combined metric value than the other comparable systems.

© All rights reserved Wu et al. and/or ACM Press

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Wu, Gang, Chang, Edward Y. and Panda, Navneet (2005): Formulating context-dependent similarity functions. In: Zhang, Hongjiang, Chua, Tat-Seng, Steinmetz, Ralf, Kankanhalli, Mohan S. and Wilcox, Lynn (eds.) Proceedings of the 13th ACM International Conference on Multimedia November 6-11, 2005, Singapore. pp. 725-734.

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Wu, Gang, Chang, Edward Y. and Zhang, Zhihua (2005): Learning with non-metric proximity matrices. In: Zhang, Hongjiang, Chua, Tat-Seng, Steinmetz, Ralf, Kankanhalli, Mohan S. and Wilcox, Lynn (eds.) Proceedings of the 13th ACM International Conference on Multimedia November 6-11, 2005, Singapore. pp. 411-414.

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Wu, Gang, Wu, Yi-Leh, Jiao, Long, Wang, Yuan-Fang and Chang, Edward Y. (2003): Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance. In: Rowe, Lawrence A., Vin, Harrick M., Plagemann, Thomas, Shenoy, Prashant J. and Smith, John R. (eds.) Proceedings of the Eleventh ACM International Conference on Multimedia November 2-8, 2003, Berkeley, CA, USA. pp. 528-538.

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