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Data-driven applications rely on the correctness of their data to functionproperly and effectively. Errors in data can be incredibly costly anddisruptive, leading to loss of revenue, incorrect conclusions, and misguidedpolicy decisions. While data cleaning tools can purge datasets of many errorsbefore the data is used, applications and users interacting with the data canintroduce new errors. Subsequent valid updates can obscure these errors andpropagate them through the dataset causing more discrepancies. Even when someof these discrepancies are discovered, they are often corrected superficially,on a case-by-case basis, further obscuring the true underlying cause, andmaking detection of the remaining errors harder. In this paper, we proposeQFix, a framework that derives explanations and repairs for discrepancies inrelational data, by analyzing the effect of queries that operated on the dataand identifying potential mistakes in those queries. QFix is flexible, handlingscenarios where only a subset of the true discrepancies is known, and robust todifferent types of update workloads. We make four important contributions: (a)we formalize the problem of diagnosing the causes of data errors based on thequeries that operated on and introduced errors to a dataset; (b) we developexact methods for deriving diagnoses and fixes for identified errors usingstate-of-the-art tools; (c) we present several optimization techniques thatimprove our basic approach without compromising accuracy, and (d) we leverage atradeoff between accuracy and performance to scale diagnosis to large datasetsand query logs, while achieving near-optimal results. We demonstrate theeffectiveness of QFix through extensive evaluation over benchmark and syntheticdata.

QFix: Diagnosing errors through query histories