Exploring knowledge benchmarking using <scp>time‐series</scp> directional distance functions and bibliometrics

Nepomuceno, Thyago Celso Cavalcante; Carvalho, Victor Diogho Heuer de; Nepomuceno, Késsia Thais Cavalcanti; Costa, Ana Paula Cabral Seixas

doi:10.1111/exsy.12967

Cited by 10 publications

(3 citation statements)

References 58 publications

(81 reference statements)

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“…The basic process is illustrated in Figure 3, and further details are presented in the following Sections (Sections 3.2-3.6). The citation structure not only involves the citation cascades but also includes vari-ous forms like main path analysis [43], the max-min method to identify core nodes in the network [44,45], and others. These technologies, especially their combination with intelligence algorithms like deep learning and machine learning, are potential methods for future studies.…”

Section: Overviews Of the Research Processsmentioning

confidence: 99%

Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades

Yu,

Liang,

Xie

2024

Mathematics

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Predicting breakthrough papers holds great significance; however, prior studies encountered challenges in this task, indicating a need for substantial improvement. We propose that the failure to capture the dynamic structural-evolutionary features of citation networks is one of the major reasons. To overcome this limitation, this paper introduces a new method for constructing citation cascades of focus papers, allowing the creation of a time-series-like set of citation cascades. Then, through a thorough review, three types of structural indicators in these citation networks that could reflect breakthroughs are identified, including certain basic topological metrics, PageRank values, and the von Neumann graph entropy. Based on the time-series-like set of citation cascades, the dynamic trajectories of these indicators are calculated and employed as predictors. Using the Nobel Prize-winning papers as a landmark dataset, our prediction method yields approximately a 7% improvement in the ROC-AUC score compared to static-based prior methods. Additionally, our method advances in achieving earlier predictions than other previous methods. The main contribution of this paper is proposing a novel method for creating citation cascades in chronological order and confirming the significance of predicting breakthroughs from a dynamic structural perspective.

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Section: Overviews Of the Research Processsmentioning

confidence: 99%

Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades

Yu,

Liang,

Xie

2024

Mathematics

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“…Such functionality can also be extended to other applications not related to bibliometric analysis. For instance, Nepomuceno et al (2023a) applied the co-occurrence analysis to interviews conducted in a bank institution to define the most relevant month-based strategies for that service unit. Each strategy defined by each employee became a keyword that composed a network with multiple interactions to support decision-making.…”

Section: Vosviewermentioning

confidence: 99%

Systematic reviews as a metaknowledge tool: caveats and a review of available options

Avenali

Daraio

Leo

et al. 2023

Int Trans Operational Res

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The development of information and communication technologies accompanied by the exponential growth of electronic scholarly publications and computer archives has led, on the one hand, to the development of techniques for the systematic analysis of published literature and, on the other hand, to a significant growth in the number of literature reviews conducted and published. A systematic literature review seeks to comprehensively identify and synthesize all studies conducted on a given topic. However, how does one ensure that the entire state of the art on a given topic has been covered? How does one collect and select a large number of publications on a given topic, and how does one analyze the knowledge extracted from these publications? In other words, how do we use systematic literature analysis to produce new knowledge from existing knowledge? To make the best use of the various tools available today for conducting systematic reviews, it is necessary to be aware of their limitations. In the paper, we provide illustrations, examples, and detailed information on the various alternatives for conducting systematic reviews, presenting a review of available sources and tools. We report an analysis of the articles published in the journal International Transactions in Operational Research to show the potential and limitations of systematic review versus domain expert‐based selection. Finally, we show that a good systematic review relies on the intelligent combination of the latest tools available for conducting systematic research with the knowledge of domain experts.

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“…The first problems associated with the data and IoT devices include constraint capabilities, intermittent loss of connection, and deployment hazards [14]. Other problems come from smart things, such as node failure, faulty nodes, data loss, network congestion, architectural flaws, and so on [15]. A third-world problem is one created by humans and launched into the deployment field to gain some benefit; examples are side-channel attacks, node capture attacks, sensor impersonation, stolen verifier attacks, Sybil attacks, etc.…”

Section: Introductionmentioning

confidence: 99%

Quality of Data (QoD) in Internet of Things (IOT): An Overview, State-of-the-Art, Taxonomy and Future Directions

Yalli,

Hasan,

Haron

et al. 2023

IJACSA

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The Internet of Things (IoT) data is the main component for finding the basis that allows decisions to be made intelligently and enables other services to be explored and used. Data originates from smart things that have the capabilities to connect and share data enormously with other things in the IoT ecosystem. However, the level of intelligence obtained and the type of services provided, all depend on whether the data is trusted or not. High-quality data is the most trusted;, it can be used to extract meaningful insights from an event and can also be used to provide good services to humans. Therefore, decisions based on high-quality and trusted data could be good, whereas those based on low-quality or untrusted data are not only bad but could also have severe consequences. The term Quality of Data (QoD) is used to represent data trustworthiness and is used throughout this paper. To the best of our knowledge, this work is the first to coin the term QoD. The problems that hinder QoD are identified and discussed. One if it is an outlier, it is a major feature of the data that degrades its overall quality. Several machine-learning techniques that detect outliers have been studied and presented, with few data-cleaning techniques. This paper aims to present the elements necessary to ensure QoD by presenting the overview of the IoT state-of-the-art. Then, data quality, data in IoT, and outliers are studied, and some quality assurance techniques that maintain data quality is presented. A comprehensive taxonomy is shown to provide state-of-the-art data in IoT. Open issues and future directions were suggested at the end of the paper.

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Exploring knowledge benchmarking using time‐series directional distance functions and bibliometrics

Cited by 10 publications

References 58 publications

Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades

Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades

Systematic reviews as a metaknowledge tool: caveats and a review of available options

Quality of Data (QoD) in Internet of Things (IOT): An Overview, State-of-the-Art, Taxonomy and Future Directions

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