DaVe: A Semantic Data Value Vocabulary to Enable Data Value Characterisation

Attard, Judie; Brennan, Rob

doi:10.1007/978-3-030-26169-6_12

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…But what is exactly meant by "data value"?. Many publications explore this term [11,12,13], but there is currently no consensus on the definition of data value, its component dimensions or on how this data value can be assessed or quantified.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Survey of Data Value: Models, Metrics, Applications and Research Challenges

Bendechache,

Attard,

Ebiele

et al. 2023

IEEE Access

Self Cite

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Data is central to modern decision making and value creation. Society creates, consumes and collects data at an increasing pace. Despite advances in processing power, data is expensive to maintain and curate. So, it is imperative to have methods and tools to distinguish between data based on its value. Yet, there is no consensus on what characterises the value of data or how this data value should be assessed. This results in heterogeneous data value models and inconsistent measurement techniques that are siloed in specific application domains. This limits the formalisation and exploitation of these concepts. We present in this paper a methodical literature analysis that discusses data value models, assessment metrics and current applications. We also highlight challenges hindering the development and exploitation of data value as concept. This leads to the identification of a set of research questions to help researchers contribute to this emerging field. The aim of this article is to stimulate further research and deployment of quantitative data value models and value-driven applications.

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Section: Introductionmentioning

confidence: 99%

A Systematic Survey of Data Value: Models, Metrics, Applications and Research Challenges

Bendechache,

Attard,

Ebiele

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

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Towards a FAIR-ready Data Value Chain for Dataspaces

Hellmanzik

Sandkuhl

2023

Lecture Notes in Business Information Processing

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Exploiting user activeness for data retention in HPC systems

Zhang

Byna

Sim

et al. 2021

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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HPC systems typically rely on the xed-lifetime (FLT) data retention strategy, which only considers temporal locality of data accesses to parallel le systems. However, our extensive analysis based on the leadership-class HPC system traces suggests that the FLT approach often fails to capture the dynamics in users' behavior and leads to undesired data purge. In this study, we propose an activeness-based data retention (ActiveDR) solution, which advocates considering the data retention approach from a holistic activeness-based perspective. By evaluating the frequency and impact of users' activities, ActiveDR prioritizes the le purge process for inactive users and rewards active users with extended le lifetime on parallel storage. Our extensive evaluations based on the traces of the prior Titan supercomputer show that, when reaching the same purge target, ActiveDR achieves up to 37% le miss reduction as compared to the current FLT retention methodology.

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DaVe: A Semantic Data Value Vocabulary to Enable Data Value Characterisation

Cited by 3 publications

References 26 publications

A Systematic Survey of Data Value: Models, Metrics, Applications and Research Challenges

A Systematic Survey of Data Value: Models, Metrics, Applications and Research Challenges

Towards a FAIR-ready Data Value Chain for Dataspaces

Exploiting user activeness for data retention in HPC systems

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