A novel visualization approach for data provenance

Yazici, Ilkay Melek; Aktaş, Mehmet S.

doi:10.1002/cpe.6523

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…[13] 2.1.3. Short Description of Technologies Used for Data Provenance Currently, there are four types of technologies [34,37,38] available for data provenance. The names for these technologies and short descriptions are provided in Figure 1 and in the sub-section.…”

Section: Sr # Usagementioning

confidence: 99%

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Data Provenance in Healthcare: Approaches, Challenges, and Future Directions

Ahmed

Dar

Helfert

et al. 2023

Sensors

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Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that make it possible to track the sources and reasons behind any problem with a user’s data. With the emergence of the General Data Protection Regulation (GDPR), data provenance in healthcare systems should be implemented to give users more control over data. This SLR studies the impacts of data provenance in healthcare and GDPR-compliance-based data provenance through a systematic review of peer-reviewed articles. The SLR discusses the technologies used to achieve data provenance and various methodologies to achieve data provenance. We then explore different technologies that are applied in the healthcare domain and how they achieve data provenance. In the end, we have identified key research gaps followed by future research directions.

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Section: Sr # Usagementioning

confidence: 99%

“…These are discussed below. For data provenance, we found four types of technologies from [34,37,38]. These can also work in combination to achieve data provenance, like in [36,39,62].…”

Section: Data Extractionmentioning

confidence: 99%

Data Provenance in Healthcare: Approaches, Challenges, and Future Directions

Ahmed

Dar

Helfert

et al. 2023

Sensors

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“…Several techniques have been proposed for provenance visual analysis. But it is still required to evaluate, summarize and compare the provenance data and discover related solutions [146], [147], [148].…”

Section: ) Provenance Visual Analysismentioning

confidence: 99%

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

et al. 2022

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Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017)(2018)(2019)(2020)(2021)(2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, webbased tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended. 13 14 ''Interactive'' and ''Web-based'' are the most searched key-42 words during journals analysis process.43 A. TAXONOMY OF DATA VISUALIZATION ARTICLES 44 The taxonomy of data visualization is laborious task. Since, 45 several data visualization techniques, tools and platforms are 46 available to generate effective and interactive visualization. 47 However, in this survey, we classify data visualization with 48 respect to various scopes and applications where advanced 49 and interactive visualization techniques are still required to 50 make better informed decisions. In the first stage, this sur-51 vey collects 70 articles from last five years (2017-2022) 52 to identify the various scopes, aspects and application of 53 data visualization. We divide the 65 articles into 7 different 54 scopes. We also classify these articles according to their 55 domain research and approaches. The contribution of each 56 article has been highlighted and cited in the Table 1. In this 57 initial stage, this survey discusses the various aspects of 58 7 scopes. In Scope-1, the techniques to handling of various 59 types of data, graphs, colours interaction and its integration 60 are discussed. In Scope-2, the focus is on the data mining 61 networks, environment and structure. In Scope-3, the role of 62 decision-making techniques in data visualization is empha-63 sised. In Scope-4, data visualization for big data and its 64 emerging applications are analysed. Scope-5 shares the visu-65 alization competencies in data security. In Scope-6, the key 66 role of statistical analysis for visualization is explored. In the 67 final Scope-7, various applications are reviewed to check 68 the performance of visualization in specific domain. The 69 contribution of each sco...

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