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Creators/Authors contains: "Li, Chenjie"

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  1. ; ; ; ; (, ACM)
    Free, publicly-accessible full text available August 3, 2026
  2. ; ; ; ; (, Proceedings of the VLDB Endowment)
    In this work, we demonstrate CaJaDE (Context-Aware Join-Augmented Deep Explanations), a system that explains query results by augmenting provenance with contextual information from other related tables in the database. Given two query results whose difference the user wants to understand, we enumerate possible ways of joining the provenance (i.e., contributing input tuples) of these two query results with tuples from other relevant tables in the database that were not used in the query. We use patterns to concisely explain the difference between the augmented provenance of the two query results. CaJaDE, through a comprehensive UI, enables the user to formulate questions and explore explanations interactively. 
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  3. ; ; ; ; (, Proceedings of the 46th International Conference on Management of Data)
    null (Ed.)
    In many data analysis applications there is a need to explain why a surprising or interesting result was produced by a query. Previous approaches to explaining results have directly or indirectly relied on data provenance, i.e., input tuples contributing to the result(s) of interest. However, some information that is relevant for explaining an answer may not be contained in the provenance. We propose a new approach for explaining query results by augmenting provenance with information from other related tables in the database. Using a suite of optimization techniques, we demonstrate experimentally using real datasets and through a user study that our approach produces meaningful results and is efficient. 
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  4. ; ; ; ; ; (, Proceedings of the VLDB Endowment)
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