Users need quick access to results of query, which is difficult to achieve with large datasets. Current query processing algorithms are often not able to satisfy users' desired running time as they require time and resource intensive pre-processing or support only limited types of queries, e.g., equi-joins. MAQS proposes a novel approach to query processing in which operators of each query collaborate and learn toghether to discover an efficient query processing strategy.

 

 

Publications

  • Multi-Agent Join  [BibTeX]
    Vahid Ghadakchi, Arash Termehchy, Mian Xiei, Bakhtiyar Doskenov, Bharghav Srikhakollu, Summit Haque, Huazheng Wang
    Technical Report (in arXiv): arXiv:2312.14291 [cs.DB], December 2023
  • Multi-Agent Join 
    Vahid Ghadakchi, Arash Termehchy, Mian Xiei, Bakhtiyar Doskenov, Bharghav Srikhakollu, Summit Haque, Huazheng Wang
    NeurIPS Workshop on ML for Systems, 2023
  • Bandit Join; Preliminary Results
    Vahid Ghadakchi, Mian Xie, Arash Termehchy
    The Proceedings of SIGMOD Workshop on AI & Data Management (aiDM), 2020.

People

  • Arash Termehchy
  • Bharghav Srikhakollu
  • Vahid Ghadakchi
  • Mian Xie
  • Bakhtiyar Doskenov
  • Huazheng Wang