Data quality in data warehouse systems: A context-based approach

Serra, Flavia; Marotta, Adriana

doi:10.1109/clei.2016.7833371

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…For this reason, we found the chosen articles according to the topic discussed and we classified these research topics into three main areas including data quality impact, technical solution in the database area and technical solution in the computer science area. Most focus has been given to the technical solution in data goodness impact and technical solution in the database area (Zellal and Zaouia, 2016;Micic et al, 2017;Abdellaoui et al, 2016;Serra and Marotta, 2016;Izham Jaya, 2019). However, just five examination articles focus on the quality of data effect.…”

Section: Data Qualitymentioning

confidence: 99%

“…The period of data quality involves terminology such as data scrubbing, data approval, data manipulation, quality data tests, data refining, data separating and tuning. It is an essential area to keep up to keep the data warehouse reliable trustworthy for business customers (Zellal and Zaouia, 2016;Serra and Marotta, 2016;Tiwari et al, 2017;Prakash and Prakash, 2017;Sokolov and Turkin, 2018;Rana, 2016). At the end of this Systematic Literature Review (SLR), we can bethink of a new approach in managing data scrubbing to produce data quality in an integrated database and data warehouse.…”

Section: Data Scrubbing Data Quality and Their Impact On Data Ware House (Dwh)mentioning

confidence: 99%

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A Systematic Literature Review for Implementing Data Ops in the Data Warehouse Lifecycle during the ETL Phase

Bahaa¹,

Eldemerdash²,

Fahmy³

2021

Journal of Computer Science

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Nowadays, no one can deny the importance of Data Ware House (DWH) in all organizations. The most important components in Data Ware House (DWH) are the Extraction, Transformation, Loading (ETL) phase. Data cleaning is a basic piece of the transformation stage in Data Warehousing. This may affect critical activities such as data collection and decision-making in various organizations Data Ops is an evaluation technique of Dev Ops in the data domain. This study conducts a Systematic Literature Review (SLR) to assess the previous studies of data warehouses related to Data Ops efforts. This study collects 55 primary studies related to the detection of Data Scrubbing, Data Consistency, Data warehouse, Dev Ops and Data Ops and we have conducted a Systematic Literature Review (SLR). Based on these findings, we discuss many concerns related to the study of current approaches in terms of abstraction level, metrics used, implementation and validation. That is why the analysis covers the published efforts between 2016 and 2021 since Data Ops is a significantly new technique. The survey should cover only research that took plan in recent years. The result of the study observed that 29% of the studies focused on solving the importance of data quality in the data warehouse, 62% of them focused on related Dev Ops, only 9% focused on Data Ops techniques and no 0% survey on enhancing ETL phase with Data Ops. This SLR brings to the attention of the research community several opportunities for using Data Ops in future research and the nearly proposed model DW Ops.

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Section: Data Qualitymentioning

confidence: 99%

Section: Data Scrubbing Data Quality and Their Impact On Data Ware House (Dwh)mentioning

confidence: 99%

A Systematic Literature Review for Implementing Data Ops in the Data Warehouse Lifecycle during the ETL Phase

Bahaa¹,

Eldemerdash²,

Fahmy³

2021

Journal of Computer Science

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“…This paper is an extension of [7], where we presented a reduced literature review and one metric for each kind of DQ defined. In this paper we present a wider literature review where we consider many more works and additionally, we present some relevant results summarized into tables.…”

Section: Introductionmentioning

confidence: 99%

Context-based Data Quality Metrics in Data Warehouse Systems

Serra¹,

Marotta²

2017

CLEIej

Self Cite

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The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through contextbased DQ metrics.

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Ind-AMSAD: A multidimensional design analysis using micro-strategy analytics desktop for Indian agriculture

Gupta¹,

Jain

2020

Journal of Statistics and Management Systems

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Data quality in data warehouse systems: A context-based approach

Cited by 3 publications

References 30 publications

A Systematic Literature Review for Implementing Data Ops in the Data Warehouse Lifecycle during the ETL Phase

A Systematic Literature Review for Implementing Data Ops in the Data Warehouse Lifecycle during the ETL Phase

Context-based Data Quality Metrics in Data Warehouse Systems

Ind-AMSAD: A multidimensional design analysis using micro-strategy analytics desktop for Indian agriculture

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