Our group thrives at the intersection of data-intensive applications and systems. We navigate the complex landscape defined by the three Vs of data: Volume, Velocity, and Variety (see our briefying slides). Our recent focus is on pioneering in the dual realms of Data Management on New Hardware and Database meets Artificial Intelligence (DB4AI & AI4DB). If you like to join our team, please first answer a few questions in questionnaire.

Data Management on New Hardware (DaMoN)

• Vision/Survey:
  • [VLDBJ’23] A Survey on Transactional Stream Processing
  • [DEBS’23] A Hardware-Conscious Stateful Stream Compression Framework for IoT Applications (Vision)
  • [SIGMOD Rec’20] Hardware-Conscious Stream Processing: A Survey
  • [OJIOT’20] NebulaStream: Complex Analytics Beyond the Cloud
• Benchmarking:
  • [SIGMOD’21] Parallelizing Intra-Window Join on Multicores: An Experimental Study
  • [ICDE’17] Revisiting the Design of Data Stream Processing Systems on Multi-Core Processors
  • [TPDS’17] Understanding Co-Running Behaviors on Integrated CPU/GPU Architectures
  • [MASCOTS’15] To Co-run, or Not to Co-run: A Performance Study on Integrated Architectures
• Algorithm/Query Optimization:
  • [ICDE’23] Scalable Online Interval Join on Modern Multicore Processors in OpenMLDB
  • [MDPI’22] Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
  • [ICDE’17] Multi-Query Optimization for Complex Event Processing in SAP ESP
• System Development/Optimization:
  • [ICDE’24 Demo] MorphStream: Scalable Processing of Transactions over Streams on Multicores
  • [ICDE’23] Parallelizing Stream Compression for IoT Applications on Asymmetric Multicores
  • [SIGMOD’23] MorphStream: Adaptive Scheduling for Scalable Transactional Stream Processing on Multicores
  • [ICDE’20] Towards Concurrent Stateful Stream Processing on Multicore Processors
  • [TPDS’21] Fine-Grained Multi-Query Stream Processing on Integrated Architectures
  • [USENIX ATC’20] FineStream: Fine-Grained Window-Based Stream Processing on CPU-GPU Integrated Architectures
  • [SIGMOD’19] BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures
  • [SC’16] Elastic Multi-resource Fairness: Balancing Fairness and Efficiency in Coupled CPU-GPU Architectures
  • [TPDS’16] Melia: A MapReduce Framework on OpenCL-Based FPGAs
  • [VLDB’14] In-Cache Query Co-Processing on Coupled CPU-GPU Architectures
  • [VLDB’13] OmniDB: towards portable and efficient query processing on parallel CPU/GPU architectures

Database meets Artificial Intelligence (DB4AI & AI4DB)

• Benchmarking:
  • [SIGMOD’23] Data Stream Clustering: An In-depth Empirical Study
• System Development/Optimization:
  • [SIGMOD’24] PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation
  • [SIGMOD’24] Predictive and Near-Optimal Sampling for View Materialization in Video Databases
  • [EMNLP’23, Main] SentiStream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams
  • [ICDE’23] CompressStreamDB: Fine-Grained Adaptive Stream Processing without Decompression
  • [VLDBJ’22] Payment behavior prediction on shared parking lots with TR-GCN
  • [TKDE’21] Periodic Weather-Aware LSTM with Event Mechanism for Parking Behavior Prediction
  • [IJCAI’20] PewLSTM: Periodic LSTM with Weather-Aware Gating Mechanism for Parking Behavior Prediction
• click to see our other ancillary topics or preprints

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At any time, we have a limited number of slots available. Unfortunately, we cannot respond to every message we receive. To help manage the process, we are using the questionnaire. We guarantee we will read the responses to this form but cannot guarantee a response. However, an impressive set of answers to this questionnaire is much more likely to result in a response than almost all other forms of contact.

If you are interested in pursuing graduate studies at NTU, please apply at the portal.