Qing Wang


Email:
Office:Building 145, Room 4.26 (map)
Mail:Research School of Computer Science
Hanna Neumann Building 145
The Australian National University
Canberra, ACT 0200
Australia
Phone:+61 (0)2 6125 4625
Fax:+61 (0)2 6125 0010
Home | Publications | Education | Projects | Resources for Students | About me

Areas of Research

I lead the Database Group at the Research School of Computer Science, ANU. My research broadly lies in the areas of databases, data mining, data modelling and knowledge reasoning. I am interested in understanding theoretical aspects of data management and analysis, and exploring their potential in supporting practitioners.

The Database Group is seeking bright, enthusiastic doctoral students. Potential applicants may contact me with your CV and academic transcripts. The scholarship is a tax-free allowance of AUD 26,288 per year, tenable for a maximum of 3.5 years.

Some Recent Work

DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
A. Wijesinghe and Q. Wang
Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), 2019.
( to appear )
Learning to Sample: an Active Learning Framework
J. Shao, Q. Wang and F. Liu
IEEE International Conference on Data Mining (ICDM), 2019.
( PDF )
CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison
Q. Zhang, A. Haller and Q. Wang
The 18th International Semantic Web Conference (ISWC), 2019.
( PDF )
Skyblocking for Entity Resolution
J. Shao, Q. Wang and Y. Lin
Information Systems, Volume 85, November 2019 (https://doi.org/10.1016/j.is.2019.06.003).
( PDF )
Knowledge Tracing with Sequential Key-Value Memory Networks
G. Abdelrahman and Q. Wang
The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2019.
( PDF )
A Highly Scalable Labelling Approach for Exact Distance Queries in Complex Networks
M. Farhan, Q. Wang, Y. Lin, and B. McKay
The 22nd International Conference on Extending Database Technology (EDBT), 2019.
( PDF )
Publishing Differentially Private Datasets via Stable Microaggregation (short paper)
M. Iftikhar, Q. Wang, and Y. Lin
The 22nd International Conference on Extending Database Technology (EDBT), 2019.
( PDF )
Repairing of Record Linkage: Turing Errors into Insight (short paper)
Q. Bui-Nguyen, Q. Wang, J. Shao, and D. Vatsalan
The 22nd International Conference on Extending Database Technology (EDBT), 2019.
( PDF )
FACH: Fast Algorithm for Detecting Cohesive Hierarchies of Communities in Large Networks
M. Rezvani, Q. Wang, and W. Liang
The 11th ACM International Conference on Web Search and Data Mining (WSDM), 2018.
( PDF )
Attentive Graph-based Recursive Neural Network for Collective Vertex Classification
Q. Xu, Q. Wang, C. Xu and L. Qu
The 26th ACM International Conference on Information and Knowledge Management (CIKM), 2017.
( PDF )
A Complete Logic for Database Abstract State Machines
F. Ferrarotti, K.-D. Schewe, L. Tec, and Q. Wang
Logic Journal of the IGPL, 2017.
( PDF )
Flower: A Data Analytics Flow Elasticity Manager
A. Khoshkbarforoushha, R. Ranjan, Q. Wang and C. Friedrich
The 43rd International Conference on Very Large Data Bases (VLDB), demo paper, 2017.
( PDF )
Improving Temporal Record Linkage using Regression Classification
Y. Hu, Q. Wang, D. Vatsalan, and P. Christen
The 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2017.
( Abstract | PDF )
Temporal Group Linkage and Evolution Analysis for Census Data
V. Christen, A. Groβ, J. Fisher, Q. Wang, P. Christen and E. Rahm
The 20th International Conference on Extending Database Technology (EDBT), 2017.
( Abstract | PDF )
Semantic-Aware LSH Blocking for Entity Resolution
Q. Wang, M. Cui and H. Liang
IEEE Transactions on Knowledge and Data Engineering (TKDE), vol 28(1), 2016.
( Abstract | PDF )
Rogas: A Declarative Framework for Network Analytics
M. Liu and Q. Wang
The 42nd International Conference on Very Large Data Bases (VLDB), demo paper, 2016.
( Abstract | PDF )
Population Informatics using Big Data (tutorial)
P. Christen, H-C. Kum, Q. Wang and D. Vatsalan
The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2016.
( Slides )
A New Thesis concerning Synchronised Parallel Computing - Simplified Parallel ASM Thesis
F. Ferrarotti, K.-D. Schewe, L. Tec and Q. Wang.
Theoretical Computer Science (TCS), 2016.
( Abstract | PDF )
A Clustering-based Framework to Control Block Size for Entity Resolution
J. Fisher, P. Christen, Q. Wang and E. Rahm
Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015.
( Abstract | PDF )
Efficient Entity Resolution with Adaptive and Interactive Training Data Selection
P. Christen, D. Vatsalan and Q. Wang.
IEEE International Conference on Data Mining (ICDM), 2015.
( Abstract | PDF )
Efficient Interactive Training Selection for Large-scale Entity Resolution
Q. Wang, D. Vatsalan and P. Christen.
The 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2015.
( Abstract | PDF | Slides )
A Theoretical Framework for Knowledge-based Entity Resolution
K.-D. Schewe and Q. Wang
Theoretical Computer Science (TCS), 2014.
( Abstract | PDF )
Network Analytics ER Model:Towards a Conceptual View of Network Analytics
Q. Wang
The 33rd International Conference on Conceptual Modeling (ER), 2014 (Best Paper Award).
( Abstract | PDF | Slides )
Data Migration: A Theoretical Perspective
B. Thalheim and Q. Wang
Data and Knowledge Engineering (DKE), Engineering, vol. 87, 260-278, 2013.
( Abstract | PDF )