Publications

2025

1. Attack as Defense: Run-time Backdoor Implantation for Image Content Protection

   In , 2025
2. under revision in VLDB

   mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

   Zhengmao Ye, Dengchun Li, Zetao Hu, and 7 more authors

   2025

2024

1. ICML

   BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks

   Zhiyuan Cheng, Zhaoyi Liu, Tengda Guo, and 4 more authors

   In Forty-first International Conference on Machine Learning, 2024
2. VLDB

   GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization

   Jiale Lao, Yibo Wang, Yufei Li, and 6 more authors

   Proc. VLDB Endow., May 2024

   Abs

   Modern database management systems (DBMS) expose hundreds of configurable knobs to control system behaviours. Determining the appropriate values for these knobs to improve DBMS performance is a long-standing problem in the database community. As there is an increasing number of knobs to tune and each knob could be in continuous or categorical values, manual tuning becomes impractical. Recently, automatic tuning systems using machine learning methods have shown great potentials. However, existing approaches still incur significant tuning costs or only yield sub-optimal performance. This is because they either ignore the extensive domain knowledge available (e.g., DBMS manuals and forum discussions) and only rely on the runtime feedback of benchmark evaluations to guide the optimization, or they utilize the domain knowledge in a limited way. Hence, we propose GPTuner, a manual-reading database tuning system that leverages domain knowledge extensively and automatically to optimize search space and enhance the runtime feedback-based optimization process. Firstly, we develop a Large Language Model (LLM)-based pipeline to collect and refine heterogeneous knowledge, and propose a prompt ensemble algorithm to unify a structured view of the refined knowledge. Secondly, using the structured knowledge, we (1) design a workload-aware and training-free knob selection strategy, (2) develop a search space optimization technique considering the value range of each knob, and (3) propose a Coarse-to-Fine Bayesian Optimization Framework to explore the optimized space. Finally, we evaluate GPTuner under different benchmarks (TPC-C and TPC-H), metrics (throughput and latency) as well as DBMS (PostgreSQL and MySQL). Compared to the state-of-the-art approaches, GPTuner identifies better configurations in 16x less time on average. Moreover, GPTuner achieves up to 30% performance improvement (higher throughput or lower latency) over the best-performing alternative.

3. VLDB

   DLRover-RM: Resource Optimization for Recommendation Models Training in Cloud

   Qinlong Wang*, Tingfeng Lan*, Yinghao Tang, and 9 more authors

   May 2024
4. SIGMOD

   A Demonstration of GPTuner: A GPT-Based Manual-Reading Database Tuning System

   Jiale Lao, Yibo Wang, Yufei Li, and 7 more authors

   In Companion of the 2024 International Conference on Management of Data, May 2024

   Abs

   Selecting appropriate values for the configurable knobs of Database Management Systems (DBMS) is crucial to improve performance. But because such complexity has surpassed the abilities of even the best human experts, database community turns to machine learning (ML)-based automatic tuning systems. However, these systems still incur significant tuning costs or only yield sub-optimal performance, attributable to their overly high reliance on black-box optimization and an oversight of domain knowledge. This paper demonstrates GPTuner, a manual-reading database tuning system that leverages Large Language Model (LLM) to bridge the gap between black-box optimization and white-box domain knowledge. This demonstration empowers (1) regular users with limited tuning experience to gain qualitative insights on the features of knobs, and optimize their DBMS performance automatically and efficiently, (2) database administrators and experts to further enhance GPTuner by simply contributing their invaluable tuning suggestions in natural language. Finally, we offer visitors the opportunity to explore a range of DBMS and optimization metrics, coupled with the flexibility to tailor their target workloads to their specific needs. Our full implementation and a video demonstration are available at https://github.com/SolidLao/GPTuner and https://youtu.be/Hz5Zck-9TlA, respectively.

5. ICDE

   Couler: Unified Machine Learning Workflow Optimization in Cloud

   Xiaoda Wang, Yuan Tang, Tengda Guo, and 6 more authors

   May 2024

2023

1. ICDE

   Cougar: A General Framework for Jobs Optimization In Cloud

   Bo Sang, Shuwei Gu, Xiaojun Zhan, and 8 more authors

   May 2023

2020

1. VAST

   Unbiased Online Sampling for Visual Exploration of Large Spatiotemporal Data

   Wang Guizhen, Guo Jingjing, Tang Mingjie, and 6 more authors

   In IEEE Conference on Visual Analytics Science and Technology (VAST 2020), May 2020
2. LocationSpark: In-memory Distributed Spatial Query Processing and Optimization

   Tang Mingjie, Yu Yongyang, Aref Walid G., and 2 more authors

   Frontiers in Big Data, section Data Mining and Management 2020, May 2020

2019

1. ICDE

   SAC: A System for Big Data Lineage Tracking

   Mingjie Tang, Saisai Shao, Weiqing Yang, and 4 more authors

   In IEEE 35th International Conference on Data Engineering (ICDE), May 2019
2. TVCG

   A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

   Huang Zhaosong, Zhao Ye, Chen Wei, and 6 more authors

   IEEE Transactions on Visualization and Computer Graphics (TVCG), May 2019
3. ICDE

   Efficient Parallel Skyline Query Processing for High-Dimensional Data

   Tang Mingjie, Yu Yongyang, Aref Walid G., and 2 more authors

   In 35th IEEE International Conference on Data Engineering (ICDE), May 2019

2018

1. TKDE

   Efficient Parallel Skyline Query Processing for High-Dimensional Data

   Mingjie Tang, Yongyang Yu, Walid G. Aref, and 2 more authors

   IEEE Transactions on Knowledge and Data Engineering, May 2018
2. SIGSPATIAL

   Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster

   Mahmood Ahmed R., Daghistani Anas, Aly Ahmed M., and 4 more authors

   In 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL), May 2018
3. CIKM

   Optimizing Generalized Linear Models with Billions of Variables

   Liang Yanbo, Yu Yongyang, Tang Mingjie, and 4 more authors

   In ACM International Conference on Information and Knowledge Management (CIKM), May 2018
4. ICDE

   SHC: Distributed Query Processing for Non-Relational Data Store

   Yang Weiqing, Tang Mingjie, Yu Yongyang, and 2 more authors

   In 34th IEEE International Conference on Data Engineering (ICDE), May 2018

2017

1. DPD

   COACT: a query interface language for collaborative databases

   Mershad K, Malluhi QM, Ouzzani M, and 4 more authors

   Distributed and Parallel Databases, May 2017
2. DPD

   AUDIT: approving and tracking updates with dependencies in collaborative databases

   Mershad K, Malluhi QM, Ouzzani M, and 3 more authors

   Distributed and Parallel Databases, May 2017
3. ICDE

   In-memory Distributed Matrix Computation Processing and Optimization

   Yu Yongyang, Tang Mingjie, Aref Walid, and 3 more authors

   In 33rd IEEE International Conference on Data Engineering (ICDE), May 2017

2016

1. VLDB

   Locationspark: A distributed in-memory data management system for big spatial data

   Mingjie Tang, Yongyang Yu, Qutaibah M Malluhi, and 2 more authors

   Proceedings of the VLDB Endowment, May 2016
2. SIGSPATIAL

   Atlas: On the Expression of Spatial-Keyword Group Queries Using Extended Relational Constructs (Systems Paper)

   Aref Walid, Mahmood Ahmed, Aly Ahmed, and 1 more author

   In 24th International Conference on Advances in Geographic Information Systems (SIGSPATIAL), May 2016
3. ICDE

   Cruncher: Distributed In-Memory Processing for Location-Based Services

   Abdelhamid Ahmed S., Tang Mingjie, Aly Ahmed M., and 4 more authors

   In 32nd IEEE International Conference on Data Engineering (ICDE), May 2016
4. TKDE

   Similarity Group-by Operators for Multi-dimensional Relational Data

   Tang Mingjie, Tahboub Ruby Y., Aref Walid G., and 4 more authors

   IEEE Transactions on Knowledge and Data Engineering (TKDE), May 2016

2015

1. EDBT

   Efficient Processing of Hamming-Distance-Based Similarity-Search Queries Over MapReduce

   Tang Mingjie, Yu Yongyang, Aref Walid G., and 2 more authors

   In 18th International Conference on Extending Database Technology (EDBT), May 2015

2014

1. SISAP

   The Similarity-aware Relational Intersect Database Operator

   Al Marri Wadha J., Malluhi Qutaibah, Ouzzani Mourad, and 2 more authors

   7th International Conference on Similarity Search and Applications (SISAP), May 2014

2013

1. IS

   Bird Flu Outbreak Prediction via Satellite Tracking

   Zhou YuanChun, Tang Mingjie, Pan Weike, and 7 more authors

   IEEE Intelligent Systems (IS) 2013, May 2013

2010

1. KAIS

   Exploring the Wild bird’s Role as Potential Vector for H5N1 transmission by Clustering and Association Analysis

   Tang Mingjie, Zhou YuanChun, Wang Weihang, and 4 more authors

   Knowledge and Information Systems (KAIS) 2010, May 2010
2. DASFAA

   Birds Bring Flues? Mining Frequent and High Weighted Cliques from Birds Migration Networks

   Tang Mingjie, Wang Weihang, Wang Yexi, and 5 more authors

   15th Database Systems for Advanced Applications (DASFAA) 2010, May 2010

2009

1. ADMA

   Discovery of Migration Habitats and Routes of Wild Bird Species by Clustering and Association Analysis

   Tang Mingjie, Wang Weihang, Zhou YuanChun, and 5 more authors

   In 5th International Conference on Advanced Data Mining and Applications (ADMA) 2009, May 2009