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教育背景

华中科技大学,博士

新加坡国立大学,博士后

研究方向

主要研究方向为大数据管理、云计算、数据隐私保护方法、数据查询处理技术。


工作经历

华中科技大学,计算机学院,副教授

研究论文

[1] Xiaofeng Ding, Hai Jin. Efficient and Progressive Algorithms for Distributed Skyline

Queries over Uncertain Data. IEEE Transactions on Knowledge and Data Engineering 

(TKDE), vol. 24, no. 8, pp. 1448-1462, Aug. 2012. (SCI)

[2] Xiaofeng Ding, Xiang Lian, Lei Chen, Hai Jin. Continuous Monitoring of Skylines over

Uncertain Data Streams. Information Sciences, Elsevier, 184(2012): 196-214. (SCI)

[3] Xiaoli Wang, Xiaofeng Ding*, Anthony K. H. Tung, Zhenjie Zhang. Efficient and Effective

KNN Sequence Search with Approximate n-grams. Accepted to appear In Proceeding of

the 40th International Conference on Very Large Data Bases (PVLDB), Pages: 1-12,

Hangzhou, China, Sep. 2014.

[4] Xiaoli Wang, Xiaofeng Ding*, Anthony K.H. Tung, Shanshan Ying, Hai Jin. An Efficient

Graph Indexing Method. In Proceeding of the 28th IEEE International Conference on

Data Engineering (ICDE'12), Pages 210-221, Arlington, VA, Apr. 2012.

[5] Xiaofeng Ding, Jianhong Jia, Jiuyong Li, Jixue Liu, and Hai Jin. Top-k Similarity Matching

in Large Graphs with Attributes. In Proceeding of the 19th International Conference on

Database Systems for Advanced Applications (DASFAA),Pages 156-170, 21-24 April, 2014, Bali, Indonesia.

[6] Xiaofeng Ding, Hai Jin, Hui Xu, Wei Song. Probabilistic Skyline Queries over Uncertain

Moving Objects. Computing and Informatics 32(5): 987-1012, 2013. (SCI)

[7] Sarowar Sattar, Jiuyong Li, Xiaofeng Ding, Jixue Liu, Millist W. Vincent. A general

framework for privacy preserving data publishing. Knowledge Based System. 54: 276-287,

2013. (SCI)

[8] Xiaofeng Ding, Qing Yu, Jiuyong Li, Jixue Liu, and Hai Jin. Distributed Anonymization

for Multiple Data Providers in a Cloud System. In Proceeding of the 18th International

Conference on Database Systems for Advanced Applications (DASFAA), Pages 346-360,

22-25 April, 2013, Wuhan, China.

[9] Weixi Xia, R. Heatherly, Xiaofeng Ding, Jiuyong Li and Bradley Malin. Efficient

Discovery of De-identification Policy Options Through a Risk-Utility Frontier. In

Proceeding of ACM CODASPY, Pages: 59-70, Feb 18–20, 2013, San Antonio, USA.

[10] 丁晓锋, 金海等. 支持频繁位置更新的不确定移动对象索引策略. 计算机学报,

2012, 35(12):2587-2597.


科研项目与获奖

暂无