Data Debugging and Exploration with Vizier

Brachmann, Mike; Bautista, Carlos; Castelo, Sonia; Su, Feng; Freire, Juliana; Glavic, Boris; Kennedy, Oliver; Mueller, Heiko; Rampin, Rémi; Spoth, William; Yang, Ying

doi:10.1145/3299869.3320246

Cited by 18 publications

(20 citation statements)

References 3 publications

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“…CheckCell [5], ExceLint [4], and Uni-Detect [55] undertake data validation errors in tabular data. Muslu et al [40] and Brachmann et al [10] furthermore consider potential problems with data curation and large data sets. Since data wrangling is an important procedure in creating visualizations, works on data linting are also valuable for later research on visualization linting.…”

Section: Linting For Visualizationmentioning

confidence: 99%

VizLinter: A Linter and Fixer Framework for Data Visualization

Chen¹,

Sun²,

Xu³

et al. 2021

Preprint

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Section: Linting For Visualizationmentioning

confidence: 99%

VizLinter: A Linter and Fixer Framework for Data Visualization

Chen¹,

Sun²,

Xu³

et al. 2021

Preprint

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“…Data debugging. Porting concepts of debugging from software to data has gained attention in data management community [10,53]. Dagger [59,60] provides data debugging primitives for white-box interactions with data-driven pipelines.…”

Section: Related Workmentioning

confidence: 99%

DataExposer: Exposing Disconnect between Data and Systems

Galhotra,

Fariha,

Lourenço

et al. 2021

Preprint

Self Cite

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

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“…Furthermore, we enable the user to specify bounds for an expression. This enables integration of AU-DBs with Lenses [16,67] to generate AU-DBs that encode the uncertainty introduced by data cleaning and curation heuristics.…”

Section: Creating Au-dbsmentioning

confidence: 99%

“…Representing uncertainty as ranges has been shown to lead to better decision-making [47]. AU-DBs can be integrated into uncertainty-aware user interfaces, e.g., Vizier [16,47].…”

Section: Introductionmentioning

confidence: 99%

Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds (extended version)

Feng,

Huber,

Glavic

et al. 2021

Preprint

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Certain answers are a principled method for coping with the uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude useful (if uncertain) answers. Prior work introduced Uncertainty Annotated Databases (UA-DBs), which combine an under-and overapproximation of certain answers. UA-DBs combine the reliability of certain answers based on incomplete K-relations with the performance of classical deterministic database systems. However, UA-DBs only support a limited class of queries and do not support attribute-level uncertainty which can lead to inaccurate underapproximations of certain answers. In this paper, we introduce attribute-annotated uncertain databases (AU-DBs) which extend the UA-DB model with attribute-level annotations that record bounds on the values of an attribute across all possible worlds. This enables more precise approximations of incomplete databases. Furthermore, we extend UA-DBs to encode an compact over-approximation of possible answers which is necessary to support non-monotone queries including aggregation and set difference. We prove that query processing over AU-DBs preserves the bounds on certain and possible answers and investigate algorithms for compacting intermediate results to retain efficiency. Through an compact encoding of possible answers, our approach also provides a solid foundation for handling missing data. Using optimizations that trade accuracy for performance, our approach scales to complex queries and large datasets, and produces accurate results. Furthermore, it significantly outperforms alternative methods for uncertain data management.

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Data Debugging and Exploration with Vizier

Cited by 18 publications

References 3 publications

VizLinter: A Linter and Fixer Framework for Data Visualization

VizLinter: A Linter and Fixer Framework for Data Visualization

DataExposer: Exposing Disconnect between Data and Systems

Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds (extended version)

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