Going beyond provenance: Explaining Query Answers with Pattern-based Counterbalances

Published

Conference Paper

© 2019 Association for Computing Machinery. Provenance and intervention-based techniques have been used to explain surprisingly high or low outcomes of aggregation queries. 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 and year can be explained by an increased number of publications in another venue in the same year. We present a novel approach 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 present efficient methods for mining such aggregate regression patterns (ARPs), discuss how to use ARPs to generate and rank explanations, and experimentally demonstrate the efficiency and effectiveness of our approach.

Full Text

Duke Authors

Cited Authors

  • Miao, Z; Zeng, Q; Glavic, B; Roy, S

Published Date

  • June 25, 2019

Published In

Start / End Page

  • 485 - 502

International Standard Serial Number (ISSN)

  • 0730-8078

International Standard Book Number 13 (ISBN-13)

  • 9781450356435

Digital Object Identifier (DOI)

  • 10.1145/3299869.3300066

Citation Source

  • Scopus