DataPrism: Exposing Disconnect between Data and Systems

Sainyam Galhotra, Anna Fariha, Raoni Lourenço, Juliana Freire, Alexandra Meliou, Divesh Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution


As data is a central component of many modern systems, the cause of a system malfunction may reside in the data, and, specifically, particular properties of data. E.g., a health-monitoring system that is designed under the assumption that weight is reported in lbs will malfunction when encountering weight reported in kilograms. Like software debugging, which aims to find bugs in the source code or runtime conditions, our goal is to debug data to identify potential sources of disconnect between the assumptions about some data and systems that operate on that data. We propose DataPrism, a framework to identify data properties (profiles) that are the root causes of performance degradation or failure of a data-driven system. Such identification is necessary to repair data and resolve the disconnect between data and systems. Our technique is based on causal reasoning through interventions: when a system malfunctions for a dataset, DataPrism alters the data profiles and observes changes in the system's behavior due to the alteration. Unlike statistical observational analysis that reports mere correlations, DataPrism reports causally verified root causes-in terms of data profiles-of the system malfunction. We empirically evaluate DataPrism on seven real-world and several synthetic data-driven systems that fail on certain datasets due to a diverse set of reasons. In all cases, DataPrism identifies the root causes precisely while requiring orders of magnitude fewer interventions than prior techniques.

Original languageEnglish (US)
Title of host publicationSIGMOD 2022 - Proceedings of the 2022 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Number of pages15
ISBN (Electronic)9781450392495
StatePublished - Jun 10 2022
Event2022 ACM SIGMOD International Conference on the Management of Data, SIGMOD 2022 - Virtual, Online, United States
Duration: Jun 12 2022Jun 17 2022

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Conference2022 ACM SIGMOD International Conference on the Management of Data, SIGMOD 2022
Country/TerritoryUnited States
CityVirtual, Online


  • causal testing
  • data profiles
  • debugging
  • root-cause identification

ASJC Scopus subject areas

  • Software
  • Information Systems


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