Guided data repair

Yakout, Mohamed; Elmagarmid, Ahmed K.; Neville, Jennifer; Ouzzani, Mourad; Ilyas, Ihab F.

doi:10.14778/1952376.1952378

Cited by 183 publications

(130 citation statements)

References 19 publications

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“…3) Model-based 2 (SCARE): This is another model-based repairing approach based on maximizing the correctness likelihood of replacement data given the data distribution, which is modelled using statistical machine learning techniques [12]. 4) Crowd-based (GuidedRepair): We implement the state-of-the-art crowdbased repairing method proposed in [13], which collects feedbacks from users to adaptively refine the training set for a repairing model. We first make a comprehensive comparison on the Precision, Recall and F1 of all the methods at an erroneous ratio of 10% on the two real data sets.…”

Section: Repairing Quality Evaluationmentioning

confidence: 99%

“…The third category of solutions are external source based repairing approaches, which leverage the information in reference master data set [5] or user's interaction data such as GuidedRepair [13] and NADEEF [3] for better data cleaning performance. However, the required external information is not always available and thus the methods can not be applied in general scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…al. [13] use user's feedback to repair a database and to adaptively refine the training set for a repairing model. However, dislike using rules, no repairing method can solve all the conflicts with one single repairing model.…”

Section: Introductionmentioning

confidence: 99%

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CrowdAidRepair: A Crowd-Aided Interactive Data Repairing Method

Zhou¹,

Li²,

Gu³

et al. 2016

Database Systems for Advanced Applications

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Abstract. Data repairing aims at discovering and correcting erroneous data in databases. Traditional methods relying on predefined quality rules to detect the conflict between data may fail to choose the right way to fix the detected conflict. Recent e↵orts turn to use the power of crowd in data repairing, but the crowd power has its own drawbacks such as high human intervention cost and inevitable low e ciency. In this paper, we propose a crowd-aided interactive data repairing method which takes the advantages of both rule-based method and crowd-based method. Particularly, we investigate the interaction between crowd-based repairing and rule-based repairing, and show that by doing crowd-based repairing to a small portion of values, we can greatly improve the repairing quality of the rule-based repairing method. Although we prove that the optimal interaction scheme using the least number of values for crowd-based repairing to maximize the imputation recall is not feasible to be achieved, still, our proposed solution identifies an e cient scheme through investigating the inconsistencies and the dependencies between values in the repairing process. Our empirical study on three data collections demonstrates the high repairing quality of CrowdAidRepair, as well as the e ciency of the generated interaction scheme over baselines.

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Section: Repairing Quality Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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CrowdAidRepair: A Crowd-Aided Interactive Data Repairing Method

Zhou¹,

Li²,

Gu³

et al. 2016

Database Systems for Advanced Applications

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“…FICSR [20] obtains user feedback in the form of query result ranking, taking into account matching constraints. Other works that also focus on data inconsistency include [26] and [14]. Our work focuses on improving correspondences at the metadata level, rather than data.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Human Effort in Reconciling Match Networks

Nguyen

Wijaya

Miklós

et al. 2013

Conceptual Modeling

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Abstract. Schema and ontology matching is a process of establishing correspondences between schema attributes and ontology concepts, for the purpose of data integration. Various commercial and academic tools have been developed to support this task. These tools provide impressive results on some datasets. However, as the matching is inherently uncertain, the developed heuristic techniques give rise to results that are not completely correct. In practice, post-matching human expert effort is needed to obtain a correct set of correspondences. We study this post-matching phase with the goal of reducing the costly human effort. We formally model this human-assisted phase and introduce a process of matching reconciliation that incrementally leads to identifying the correct correspondences. We achieve the goal of reducing the involved human effort by exploiting a network of schemas that are matched against each other. We express the fundamental matching constraints present in the network in a declarative formalism, Answer Set Programming that in turn enables to reason about necessary user input. We demonstrate empirically that our reasoning and heuristic techniques can indeed substantially reduce the necessary human involvement.

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“…Recently, [19] puts forward a system for guiding data repairing that explicitly involves the user in the process of checking the data repairs automatically produced by the algorithms introduced in [5]. In particular, the authors focused on ranking the repairs in such a way that the user effort spent in analyzing useless information is minimized.…”

Section: Related Workmentioning

confidence: 99%

Support for User Involvement in Data Cleaning

Galhardas

Lopes

Santos

2011

Data Warehousing and Knowledge Discovery

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Abstract. Data cleaning and ETL processes are usually modeled as graphs of data transformations. The involvement of the users responsible for executing these graphs over real data is important to tune data transformations and to manually correct data items that cannot be treated automatically. In this paper, in order to better support the user involvement in data cleaning processes, we equip a data cleaning graph with data quality constraints to help users identifying the points of the graph and the records that need their attention and manual data repairs for representing the way users can provide the feedback required to manually clean some data items. We provide preliminary experimental results that show the significant gains obtained with the use of data cleaning graphs.

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Guided data repair

Cited by 183 publications

References 19 publications

CrowdAidRepair: A Crowd-Aided Interactive Data Repairing Method

CrowdAidRepair: A Crowd-Aided Interactive Data Repairing Method

Minimizing Human Effort in Reconciling Match Networks

Support for User Involvement in Data Cleaning

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