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Featured Researchers

Professor Jianliang Xu

Acting Associate Dean of Science (Research)
Professor, Department of Computer Science

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Professor Jianliang Xu

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About

Professor Xu has been working on innovative research and development in the subject field of computer science. He has published more than 200 technical papers and made significant contributions to the areas of big data management, blockchain, mobile computing, data security and privacy. With grants totalling more than HK$20 million, his research has been continuously funded by the Research Grants Council (RGC) of Hong Kong, National Natural Science Foundation of China (NSFC), Hong Kong’s Innovation and Technology Fund (ITF), Croucher Foundation, K.C. Wong Education Foundation and the Tin Ka Ping Foundation. In particular, he has acted as Principal and Co-principal Investigator for 17 RGC projects.

 

Selected ongoing projects:

  • Searchable Blockchain. Blockchain has recently emerged as a promising solution for providing trustworthy storage and computation for decentralised applications. While blockchain technology has many advantages such as immutability and traceability, its resource-consuming consensus mechanism limits the performance and scalability. To scale blockchain systems, one can leverage third-party services to outsource computation and storage tasks. However, since third parties cannot be fully trusted, these outsourcing models have risks related to losing data integrity. To tackle these issues, this project aims at investigating techniques and algorithms to enable efficient integrity-assured query and storage services over blockchain databases.
  • Geospatial Data Analytics. Spatial influence analytics is often used to find the influence zone of a spatial facility with respect to certain spatial criteria. Owing to its broad applications in decision support, market analysis, social media advertising and recommender systems, the problem of spatial influence analysis has been studied extensively in literature. However, most of the existing work focuses on spatial proximity only. With the rapid development of location-based services (LBS), a large number of spatial facilities have been enriched with text information – for example, categorical descriptions or user-generated reviews. This project proposes to investigate a novel paradigm – keyword-based spatial influence analytics – to study the influence zone of a spatial-textual facility on the basis of both spatial proximity and textual similarity. 

Achievements

  • HKBU President's Award for Outstanding Performance in Scholarly Work (2016-17)

 

Research Outputs

  • Liu, Q., M. Zhao, X. Huang, J. Xu & Y. Gao. “Truss-based Community Search over Large Directed Graphs.” Proceedings of the ACM SIGMOD International Conference on Management of Data (2020).
  • Xu, C., C. Zhang & J. Xu. “vChain: Enabling Verifiable Boolean Range Queries over Blockchain Databases.”  Proceedings of the ACM SIGMOD International Conference on Management of Data (2019).
  • Xu, C., J. Xu, H. Hu & M. H. Au. “When Query Authentication Meets Fine-Grained Access Control: A Zero-Knowledge Approach.” Proceedings of the ACM SIGMOD International Conference on Management of Data (2018).
  • Chen, L., Y. Li, J. Xu & Christian S. Jensen. “Towards Why-Not Spatial Keyword Top-k Queries: A Direction-Aware Approach.” IEEE Transactions on Knowledge and Data Engineering 30.4 (2018): 796-809.
  • Zhu, Q., H. Hu, C. Xu, J. Xu & W. C. Lee. “Geo-Social Group Queries with Minimum Acquaintance Constraints.”  Very Large Data Bases Journal 26.5 (2017): 709-727.
  • Yi, P., B. Choi, S. S. Bhowmick & J. Xu. “AutoG: A Visual Query Autocompletion Framework for Graph Databases.”  Very Large Data Bases Journal 26.3 (2017): 347-372.
  • Chen, R., Q. Xiao, Y. Zhang & J. Xu. “Differentially Private High-Dimensional Data Publication via Sampling-based Inference.” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015).
  • Chen, Q., H. Hu & J. Xu. “Authenticated Online Data Integration Services.” Proceedings of the ACM SIGMOD International Conference on Management of Data (2015).
  • Chen, Q., H. Hu & J. Xu. “Authenticating Top-k Queries in Location-based Services with Confidentiality.” Proceedings of the 40th International Conference on Very Large Databases (2014).
  • Hu, H., J. Xu, Q. Chen & Z. Yang. “Authenticating Location-based Services without Compromising Location Privacy.” Proceedings of the ACM SIGMOD International Conference on Management of Data (2012).