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Creators/Authors contains: "Zhang, Qin"

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  1. ; ( , Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-24))
    Free, publicly-accessible full text available April 1, 2025
  2. ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    Free, publicly-accessible full text available February 20, 2025
  3. ; ; ( , The VLDB Journal)
    Free, publicly-accessible full text available March 1, 2025
  4. ; ( , Proceedings of the VLDB Endowment)
    This paper introduces SyncSignature, the first fully parallelizable algorithmic framework for tree similarity joins under edit distance. SyncSignature makes use of implicit-synchronized signature generation schemes, which allow for an efficient and parallelizable candidate-generation procedure via hash join. Our experiments on large real-world datasets show that the proposed algorithms under the SyncSignature framework significantly outperform the state-of-the-art algorithm in the parallel computation environment. For datasets with big trees, they also exceed the state-of-the-art algorithms by a notable margin in the centralized/single-thread computation environment. To complement and guide the experimental study, we also provide a thorough theoretical analysis for all proposed signature generation schemes. 
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    Free, publicly-accessible full text available August 28, 2024
  5. ; ; ; ; ; ; ; ; ; ; et al ( , Nano Letters)
    Free, publicly-accessible full text available August 23, 2024
  6. ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ( , Atmospheric Research)
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  8. ; ( , The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22))
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  9. ; ; ( , IFAC-PapersOnLine)
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  10. ; ; ; ; ; ; ( , IEEE Transactions on Cloud Computing)
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