Qing Wang

Office:Building 115, Room B245 (map)
Mail:Research School of Computer Science
RSISE Building 115
The Australian National University
Canberra, ACT 0200
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

A Complete Logic for Database Abstract State Machines
F. Ferrarotti, K.-D. Schewe, L. Tec, and Q. Wang
Logic Journal of the IGPL, 2017.
(to appear)
Flower: A Data Analytics Flow Elasticity Manager
A. Khoshkbarforoushha, R. Ranjan, Q. Wang and C. Friedrich
43rd International Conference on Very Large Data Bases (VLDB), demo paper, 2017.
(to appear)
Improving Temporal Record Linkage using Regression Classification
Y. Hu, Q. Wang, D. Vatsalan, and P. Christen
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
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
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
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.
roceedings of 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
Proceedings of 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 )