On Detecting Cherry-picked Generalizations

Yin Lin, Brit Youngmann, Yuval Moskovitch, H. V. Jagadish, Tova Milo

Research output: Contribution to journalConference articlepeer-review

Abstract

Generalizing from detailed data to statements in a broader context is often critical for users to make sense of large data sets. Correspondingly, poorly constructed generalizations might convey misleading information even if the statements are technically supported by the data. For example, a cherry-picked level of aggregation could obscure substantial sub-groups that oppose the generalization. We present a framework for detecting and explaining cherry-picked generalizations by refining aggregate queries. We present a scoring method to indicate the appropriateness of the generalizations. We design efficient algorithms for score computation. For providing a better understanding of the resulting score, we also formulate practical explanation tasks to disclose significant counterexamples and provide better alternatives to the statement. We conduct experiments using real-world data sets and examples to show the effectiveness of our proposed evaluation metric and the efficiency of our algorithmic framework.

Original languageEnglish
Pages (from-to)59-71
Number of pages13
JournalProceedings of the VLDB Endowment
Volume15
Issue number1
DOIs
StatePublished - 2021
Event48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australia
Duration: 5 Sep 20229 Sep 2022

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