Rule-Based Multidimensional Data Quality Assessment Using Contexts

Marotta, Adriana; Vaisman, Alejandro

doi:10.1007/978-3-319-43946-4_20

Cited by 13 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, [24] presents a model of the asset management data infrastructure and [33] a Bayesian Network model that shows how DQ can be improved while satisfying budget constraints. In the case of [4], it presents a formal multidimensional model on which is applied a rule-based approach to DQ assessment, and [42] provides a cost and value model for issues related to DQ. Finally, [28] models context for source selection in Linked Data, and [3] proposes a conceptual model where the relationships among quality characteristics of e-government websites and users' perceptions are represented.…”

Section: Review Resultsmentioning

confidence: 99%

“…Another category concern frameworks for assessing data or information quality [4,6,11,20,28,42,43,55,60,62,67,68,72,85,89]. For instance, in [28] the authors use a framework called Luzzu [34] for linked DQ assessment.…”

Section: Review Resultsmentioning

confidence: 99%

“…For instance, in [28] the authors use a framework called Luzzu [34] for linked DQ assessment. Additionally, [4] provides a framework for context-aware Data Warehouse quality assessment. Other proposals that are task-specific, as [33] that presents a framework for discovering dependencies between DQ dimensions, or [26] that proposes a framework that encapsulates data cleaning procedures.…”

Section: Review Resultsmentioning

confidence: 99%

“…are also taken into account for contextualizing DQ dimensions [5,14,25,43,48,49,60,62,67,68,85]. In turn, data in use or information are also used to give context to DQ dimensions [4,10,11,28,33,81], and according to some PS, DQ dimensions can be influenced by rules and constraints of the application domain [9,23,72,77]. On the other hand, some authors affirm that DQ requirements condition DQ dimensiones [51,75,82].…”

Section: Review Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Use of Context in Data Quality Management: a Systematic Literature Review

Serra¹,

Peralta²,

Marotta³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The importance of context in data quality (DQ) was shown many years ago and nowadays is widely accepted. Early approaches and surveys defined DQ as fitness for use and showed the influence of context on DQ. This paper presents a Systematic Literature Review (SLR) for investigating how context is taken into account in recent proposals for DQ management. We specifically present the planning and execution of the SLR, the analysis criteria and our results reflecting the relationship between context and DQ in the state of the art and, particularly, how that context is defined and used for DQ management.CCS Concepts: • Data Quality → Context in Data Quality Management.

show abstract

Section: Review Resultsmentioning

confidence: 99%

Section: Review Resultsmentioning

confidence: 99%

Section: Review Resultsmentioning

confidence: 99%

Section: Review Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Use of Context in Data Quality Management: a Systematic Literature Review

Serra¹,

Peralta²,

Marotta³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The dynamicity of web sources [21], lack of reliability [22] and poor quality of the web data [23] are the major challenges in incorporating the web data into the DW. Maintaining the quality of DW [24] is of utmost importance. Hence, it is most crucial to evaluate the identified set of web data sources based on certain criteria of information quality [25–29] and then select high quality, credible, compatible sources from amongst them.…”

Section: Introductionmentioning

confidence: 99%

WSEM _QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

2020

View full text Add to dashboard Cite

The incorporation of suitable external data from the World Wide Web offers an effective solution for enriching the data in the data warehouse (DW). However, the main challenge is the quality‐aware selection of web data sources to maintain the quality of the DW. In the previous works, the quality evaluation of web sources is through expert evaluation only, which makes it a very lengthy process. Also, since the quality model consists of mixed quality factors from diverse domains of Web, DW and underlying business, finding an expert possessing an expertise of all these domains is a huge bottleneck in the evaluation process. In order to overcome these existing issues, this study proposes a novel multi‐level approach web source evaluation with multi‐criteria decision‐making and web quality testing tools (WSEMQT) and underlying quality model web quality model for evaluating web sources for the DW. The authors introduce automated web source quality evaluation in the first level of web source based evaluation and multiple dimensions of quality evaluation at the second level of expert‐based evaluation. At both the levels, multi‐criteria decision‐making methods are applied to the evaluation scores obtained to ascertain the ranked list of Web sources. The authors present a real‐world academic web data case study which shows that the proposed approach can be executed successfully for real‐world problems.

show abstract