LinCQA: Faster Consistent Query Answering with Linear Time Guarantees
Zhiwei Fan, Paraschos Koutris, Xiating Ouyang, Jef Wijsen
摘要
Most data analytical pipelines often encounter the problem of querying inconsistent data that violate pre-determined integrity constraints. Data cleaning is an extensively studied paradigm that singles out a consistent repair of the inconsistent data. Consistent query answering (CQA) is an alternative approach to data cleaning that asks for all tuples guaranteed to be returned by a given query on all (in most cases, exponentially many) repairs of the inconsistent data. In this paper, we identify a class of acyclic select-project-join (SPJ) queries for which CQA can be solved via SQL rewriting with a linear time guarantee. Our rewriting method can be viewed as a generalization of Yannakakis' algorithm for acyclic joins to the inconsistent setting. We present LinCQA, a system that takes as input any query in our class and outputs rewritings in both SQL and non-recursive Datalog with negation. We show that LinCQA often outperforms the existing CQA systems on both synthetic and real-world workloads, and in some cases, by orders of magnitude.
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- CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification TasksPeng Li, Xi Rao, Jennifer Blase, Yue Zhang 等ICDE 2021 · 被引用 127 次
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- Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain PredictionsBojan Karlas, Peng Li, Renzhi Wu, Nezihe Merve Gürel 等VLDB 2021 · 被引用 69 次
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