Open Source Data Quality Tools: Revisited

Pulla, Venkata Sai Venkatesh; Varol, Cihan; et.al, M.P.Karthikeyan

doi:10.1007/978-3-319-32467-8_77

Cited by 5 publications

(13 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the authors list 9 usability criteria for the GUI, but in the evaluation they only distinguish between (g) representing “not user friendly GUI” and a (G) for “user-friendly GUI” with drag and drop functionality. Pulla et al ( 2016 ) published a revised version of the tool overview, which is very similar to the original work in terms of structure and methodology. They used the same criteria structure as Pushkarev et al ( 2010 ), but omitted the data monitoring group [since it is not provided by any of the tools according to Pulla et al ( 2016 )] and 4 other sub-features without further justification.…”

Section: Survey Methodologymentioning

confidence: 99%

“…Pulla et al ( 2016 ) published a revised version of the tool overview, which is very similar to the original work in terms of structure and methodology. They used the same criteria structure as Pushkarev et al ( 2010 ), but omitted the data monitoring group [since it is not provided by any of the tools according to Pulla et al ( 2016 )] and 4 other sub-features without further justification. The list of investigated DQ tools was extended from 7 to 10.…”

Section: Survey Methodologymentioning

confidence: 99%

“…The list of investigated DQ tools was extended from 7 to 10. Our survey differs notably from these two papers since, we conducted a systematic search to select DQ tools and also investigated commercial tools, while Pushkarev et al, 2010 and (Pulla et al, 2016 ) presented a predefined selection of free or open-source tools without publishing their selection strategy. Moreover, we focus on data profiling, DQ measurement, and DQ monitoring and evaluate these feature groups with a more detailed and comprehensive criteria catalog as provided by other published surveys mentioned above.…”

Section: Survey Methodologymentioning

confidence: 99%

“…The aim of this survey is to observe not only the functionalities of current DQ tools in terms of data profiling and measurement, but also in terms of true DQ monitoring. Pushkarev et al ( 2010 ) and a follow-up study (Pulla et al, 2016 ) point out that none of the tools observed had any monitoring functionality. We however want to include this criterion in our requirements catalog since there is evidence on several DQ tool websites that they do offer monitoring functionalities, but have not been observed by Pushkarev et al ( 2010 ) and Pulla et al ( 2016 ).…”

Section: Theoretical Background On Data Qualitymentioning

confidence: 99%

“…Pushkarev et al ( 2010 ) and a follow-up study (Pulla et al, 2016 ) point out that none of the tools observed had any monitoring functionality. We however want to include this criterion in our requirements catalog since there is evidence on several DQ tool websites that they do offer monitoring functionalities, but have not been observed by Pushkarev et al ( 2010 ) and Pulla et al ( 2016 ). According to the ISO standard 8,000 ( ISO 8000-8:2015(E ), 2015 ), pragmatic data quality measurement requires interaction with the respective users who validate the data.…”

Section: Theoretical Background On Data Qualitymentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Data Quality Measurement and Monitoring Tools

Ehrlinger

Wöß

2022

Front. Big Data

View full text Add to dashboard Cite

High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of data quality issues and different generally applicable data quality dimensions and metrics have been discussed. In this work, we close the gap between data quality research and practical implementations with a detailed investigation on how data quality measurement and monitoring concepts are implemented in state-of-the-art tools. For the first time and in contrast to all existing data quality tool surveys, we conducted a systematic search, in which we identified 667 software tools dedicated to “data quality.” To evaluate the tools, we compiled a requirements catalog with three functionality areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) automated data quality monitoring. Using a set of predefined exclusion criteria, we selected 13 tools (8 commercial and 5 open-source tools) that provide the investigated features and are not limited to a specific domain for detailed investigation. On the one hand, this survey allows a critical discussion of concepts that are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable data quality metrics. On the other hand, it reveals potential for functional enhancement of data quality tools and supports practitioners in the selection of appropriate tools for a given use case.

show abstract

Section: Survey Methodologymentioning

confidence: 99%

Section: Survey Methodologymentioning

confidence: 99%

Section: Survey Methodologymentioning

confidence: 99%

Section: Theoretical Background On Data Qualitymentioning

confidence: 99%

Section: Theoretical Background On Data Qualitymentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Data Quality Measurement and Monitoring Tools

Ehrlinger

Wöß

2022

Front. Big Data

View full text Add to dashboard Cite

show abstract

The Effect of Data Characteristics and Top Management Characteristics on Decision Making Capabilities: The Role of AI and Business Analytical Capability

Alarefi

2022

Wseas Transactions on Information Science and Applications

View full text Add to dashboard Cite

Data is essential for making decisions. However, these data should be appropriately collected and analyzed by capable managers. Few studies examined the effect of data characteristics (DC) and top management characteristics (TMC) on decision-making capability (DMC). In addition, few examine the application of artificial intelligence enterprise resource planning (AIERP) in this process. The purpose of this study is to examine the effect of DC and TMC on DMC. Building on existing theories and studies, this study proposed that DC (data integrity, data quality, data authentication, and data error) significantly affect the DMC. In addition, TMC (data analytical capability and technological innovation) significantly affect the DMC. AIERP is predicted to have a mediator role between DC and TMC, and DMC. Business analytical capability (BAC) is anticipated as a moderating variable. The data was collected from technological companies in the Gulf Cooperation Council (GCC). A purposive sampling technique was deployed. The findings using SmartPLS 4.0 showed that DC and its components expect data authentication and TMC and its components have significant effects on DMC. AIERP mediated the effect of DC and TMC on DMC while BAC did not moderate the effect of DC and TMC on DMC. Decision-makers have to focus on collecting high-quality data and ensuring the data is free from error. Decision-makers also have to use technology to enhance the quality and effectiveness of decisions.

show abstract

Predictors of Blockchain Technology Acceptance in Medical Imaging: The Mediating Role of Initial Trust

Al-Sulami,

Ali,

Ramli

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Blockchain technology (BCT) is an emerging technology that has been used mainly in supply chain and financial technology. However, the use of this technology in Medical Imaging (MI) is still limited. This study investigates the acceptability of BCT in MI in public hospitals in Iraq. Based on relevant theories, the study proposed that Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), and Facilitating Condition (FC) significantly affect the acceptability of BCT in MI. Similarly, EE, PE, SI, and FC are expected to affect Initial Trust (IT), which in turn is proposed to mediate the effect of EE, PE, SI, and FC on the acceptability of BCT in MI. Data were collected from 136 doctors from public hospitals in Iraq. The results indicated that EE, PE, and SI positively affected the acceptability of BCT in MI. EE and PE positively affected IT. Furthermore, IT positively affected the acceptability of BCT in MI and mediated the effect of EE and PE. The study offers valuable insights for both theoretical and practical implementations, can guide future research, and informs strategies for the effective acceptability of BCT in MI in public hospitals.

show abstract

Open Source Data Quality Tools: Revisited

Cited by 5 publications

References 3 publications

A Survey of Data Quality Measurement and Monitoring Tools

A Survey of Data Quality Measurement and Monitoring Tools

The Effect of Data Characteristics and Top Management Characteristics on Decision Making Capabilities: The Role of AI and Business Analytical Capability

Predictors of Blockchain Technology Acceptance in Medical Imaging: The Mediating Role of Initial Trust

Contact Info

Product

Resources

About