Towards Reusing Data Cleaning Knowledge

Almeida, Ricardo; Maio, Paulo; Oliveira, Paulo; Barroso, João

doi:10.1007/978-3-319-16486-1_14

Cited by 1 publication

(3 citation statements)

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“…The Data Cleaning Methodology proposed in (Almeida et al, 2015) relies and promotes the following principles: (i) the specification of Data Cleaning Operations (DCOs) for a given data source (say DS 1 ) should be carried on by a domain expert through an application operating as much as possible closer to the human conceptual level and (ii) to better assist the domain expert it is able to take advantage of data cleaning knowledge (including DCOs) specified previously on the context of another data source (say DS 2 ) whose domain partially overlaps with DS 1 domain, even when DS 1 and DS 2 have a different data schema and/or model. In the following, this methodology (depicted in Figure 1) is briefly described and complemented with a running example based on the scenario introduced in Example 1.…”

Section: Data Cleaning Methodologymentioning

confidence: 99%

“…Previously, we have proposed a novel and generic data cleaning methodology that intends to assist the domain expert in the specification of the cleaning operations (Almeida et al, 2015). The methodology reuses cleaning knowledge previously specified for other data sources, even if those sources have different data models and/or schemas.…”

Section: Introductionmentioning

confidence: 99%

“…The paper is organized as follows. Section 2 briefly describes and exemplifies the data cleaning methodology (Almeida et al, 2015). In Section 3, the requirements that a Data Cleaning Operations Vocabulary (DCOV) must satisfy are described in detail.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Ontology-based Methodology for Reusing Data Cleaning Knowledge

Almeida¹,

Maio²,

Oliveira

et al. 2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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The organizations' demand to integrate several heterogeneous data sources and an ever-increasing volume of data is revealing the presence of quality problems in data. Currently, most of the data cleaning approaches (for detection and correction of data quality problems) are tailored for data sources with the same schema and sharing the same data model (e.g., relational model). On the other hand, these approaches are highly dependent on a domain expert to specify the data cleaning operations. This paper extends a previously proposed data cleaning methodology that reuses cleaning knowledge specified for other data sources. The methodology is further detailed/refined by specifying the requirements that a data cleaning operations vocabulary must satisfy. Ontologies in RDF/OWL are proposed as the data model for an abstract representation of the data schemas, no matter which data model is used (e.g., relational; graph). Existing approaches, methods and techniques that support the implementation of the proposed methodology, in general, and specifically of the data cleaning operations vocabulary are also presented and discussed in this paper.

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Section: Data Cleaning Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%