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Provenance and intervention-based techniques have been used to explain surprising high or low outcomes of aggregation queries based on the outcome’s provenance. However, such techniques may miss interesting explanations emerging from data that is not in the provenance. For instance, an unusually low number of publications of a prolific researcher in a certain venue in a year can be explained by an increase in his publication in another venue in the same year. In this project we investigate how to mine patterns that describe inherent trends in the data and to use these patterns to identify potential causes for an outcome of interest.

As an initial contribution we have developed a novel system called Cape (Counterbalancing with Aggregation Patterns for Explanations) for explaining outliers in aggregation queries through counterbalancing. That is, explanations are outliers in the opposite direction of the outlier of interest. Outliers are defined w.r.t. patterns that hold over the data in aggregate. We have developed efficient methods for mining such aggregate regression patterns (ARPs) and have demonstrated how to use ARPs to generate and rank explanations.



  1. Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances
    Zhengjie Miao, Qitian Zeng, Boris Glavic and Sudeepa Roy
    Proceedings of the 44th International Conference on Management of Data (2019).
  2. CAPE: Explaining Outliers by Counterbalancing
    Zhengjie Miao, Qitian Zeng, Chenjie Li, Boris Glavic, Oliver Kennedy and and Sudeepa Roy
    Proceedings of the VLDB Endowment (Demonstration Track). (2019).