Information Management Technologies for Big Data

Kaloyanova, Kalinka; Hristov, Tsvetomir; Naydenova, Ina; Kovacheva, Zlatinka

doi:10.1145/3231830.3231840

Cited by 4 publications

(7 citation statements)

References 2 publications

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“…As Table 1 shows, the selected studies were undertaken in Nigeria ( n = 1), 18 Canada ( n = 4), 12 19 20 21 United States ( n = 6), 22 23 24 25 26 Kenya ( n = 2), 27 28 Iran ( n = 4), 29 30 31 32 Saudi Arabia ( n = 1), 33 China ( n = 1), 34 Tanzania ( n = 1), 35 Ethiopia ( n = 1), 36 Bulgaria ( n = 1), 37 and Australia ( n = 2). 38 39 In terms of research methodology, the use of quantitative methods ( n = 14) 12 18 19 20 21 27 28 31 32 33 34 35 38 39 was more common than qualitative research methods ( n = 7) 22 23 25 26 29 30 37 to assess different data quality dimensions. Two studies used the mixed-methods research approach to assess data quality in health care.…”

Section: Resultsmentioning

confidence: 99%

“…The degree to which data attributes have no conflict and are consistent with other data and their attributes. 37…”

Section: Resultsmentioning

confidence: 99%

“…In some studies, researchers developed a specific tool, model, or framework for data quality assessment. 12 20 22 23 24 28 30 34 35 37 Although this approach could help other researchers conduct future studies and compare their results, these tools or models may not be useful to assess quality of data in all databases. Moreover, few studies investigated respondents' opinions about data quality in health care settings; however, this type of research can be useful to gain a deeper understanding of the reasons for generating low-quality data and can help provide suitable improvement strategies.…”

Section: Synthesismentioning

confidence: 99%

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Data Quality in Health Care: Main Concepts and Assessment Methodologies

et al. 2023

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Introduction In the health care environment, a huge volume of data is produced on a daily basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health data usually face with numerous challenges that lead to producing incomplete, inaccurate, and untimely data. As a result, data quality issues have received more attention than before. Objective The purpose of this article is to provide an insight into the data quality definitions, dimensions, and assessment methodologies. Methods In this article, a scoping literature review approach was used to describe and summarize the main concepts related to data quality and data quality assessment methodologies. Search terms were selected to find the relevant articles published between January 1, 2012 and September 31, 2022. The retrieved articles were then reviewed and the results were reported narratively. Results In total, 23 papers were included in the study. According to the results, data quality dimensions were various and different methodologies were used to assess them. Most studies used quantitative methods to measure data quality dimensions either in paper-based or computer-based medical records. Only two studies investigated respondents' opinions about data quality. Conclusion In health care, high-quality data not only are important for patient care, but also are vital for improving quality of health care services and better decision making. Therefore, using technical and nontechnical solutions as well as constant assessment and supervision is suggested to improve data quality.

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Section: Resultsmentioning

confidence: 99%

“…The degree to which data attributes have no conflict and are consistent with other data and their attributes. 37…”

Section: Resultsmentioning

confidence: 99%

Section: Synthesismentioning

confidence: 99%

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Data Quality in Health Care: Main Concepts and Assessment Methodologies

et al. 2023

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Section: Methods Detailsmentioning

confidence: 99%

“…Evaluating data quality metrics from prospective registries has gained great prominence in the literature in recent years [9 , 10] . Although there is no consensus on the main metrics which should be adopted, the most cited indicators include completeness, accuracy, and temporal plausibility [11] , [12] , [13] . At the same time, direct data auditing, which involves comparing collected data with primary information sources, has been adopted as an additional strategy to ensure the quality of prospective clinical registries [14 , 15] .…”

Section: Methods Detailsmentioning

confidence: 99%

The impact of data quality monitoring of a multicenter prospective registry of cardiac implantable electronic devices

Saucedo,

Silva,

Silva

et al. 2023

MethodsX

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Utilizing Big Data for Enhancing Passenger Safety in Railway Stations

Alawad

Kaewunruen

2019

IOP Conf. Ser.: Mater. Sci. Eng.

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In light of the increasing demand and capacity in the railway industry, it is imperative to maintain safety in relation to the complexities of the substantial railway stations. Thus, it is important to take note of the time where investments in new technologies directed at the safety of the railway enable safety and protection in this area. Novel technological techniques such as big data analysis (BDA), data mining or machine learning (ML) have been developed and applied in many areas such as sales, banking and healthcare. The development of such methods has important benefits within the context of railway safety, however, these new methods need to be implemented and developed with consideration of whether these operational models can help to solve the various difficulties that currently exist in the risk analysis of railway stations. Moreover, as the adoption of the Internet of thing (IoT) grows, it is expected that analytical needs for handling data will also increase. It has been shown that the progression towards automation and applying such innovative new technologies such as BDA may be a powerful tool for integration in the future of transportation in general and the railway industry in particular, whereby analytical predictions can aid in the development of safer railway stations which have greater potential for ensuring the safety of passengers. In this paper a Bow Tie (BT) framework model has been created to combine BDA into the risk assessment process. The BDA can be beneficial to the risk assessment, support the decision makers in real time, and reduce human errors. This method can be fully integrated into passenger data and the business model for the railway station. Employing the existing safety records utilizing BDA is expected to mitigate risks, predict hazards, raise safety and security efficiency and reduce the cost.

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Information Management Technologies for Big Data

Cited by 4 publications

References 2 publications

Data Quality in Health Care: Main Concepts and Assessment Methodologies

Data Quality in Health Care: Main Concepts and Assessment Methodologies

The impact of data quality monitoring of a multicenter prospective registry of cardiac implantable electronic devices

Utilizing Big Data for Enhancing Passenger Safety in Railway Stations

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