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CAPE: Explaining Outliers by Counterbalancing

Authors

Materials

Abstract

In this demonstration we showcase Cape, a system that ex- plains surprising aggregation outcomes. In contrast to previous work which relies exclusively on provenance, Cape applies a novel approach for explaining outliers in aggregation queries through counterbalancing (outliers in the opposite direction). The foundation of our approach are aggregate regression patterns (ARPs) based on which we defined outliers, and an efficient explanation generation algorithm that utilizes these patterns. In the demonstration, the audience can run aggregation queries over real world datasets, and browse the patterns and explanations returned by Cape for outliers in the result of such queries.

bibtex

@article{MZ19a,
  author = {Miao, Zhengjie and Zeng, Qitian and Li, Chenjie and Glavic, Boris and Kennedy, Oliver and Roy, Sudeepa},
  date-modified = {2019-08-02 09:14:13 -0500},
  journal = {Proceedings of the VLDB Endowment (Demonstration Track)},
  keywords = {Outliers; Intervention; Cape},
  pdfurl = {http://www.vldb.org/pvldb/},
  projects = {Cape},
  title = {CAPE: Explaining Outliers by Counterbalancing},
  venueshort = {{PVLDB}},
  year = {2019}
}

Reference

CAPE: Explaining Outliers by Counterbalancing Zhengjie Miao, Qitian Zeng, Chenjie Li, Boris Glavic, Oliver Kennedy and Sudeepa Roy Proceedings of the VLDB Endowment (Demonstration Track). (2019).