The Role of Data Science in Web Science

Phethean, Christopher; Simperl, Elena; Tiropanis, Thanassis; Tinati, Ramine; Hall, Wendy

doi:10.1109/mis.2016.54

Cited by 20 publications

(6 citation statements)

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“…It can also be discretized through machine learning, and sample learning makes the discretization results more objective and reasonable. The essence of the discretization process is to give some partition points to divide the attribute space [ 14 ].…”

Section: Design Principle and Technology Of Packaging Big Data Inform...mentioning

confidence: 99%

Packaging Big Data Visualization Based on Computational Intelligence Information Design

Zhang

2022

Computational Intelligence and Neuroscience

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A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the appropriate stopping conditions to avoid overcrowding. The effectiveness of the improved algorithm has been experimentally proven. Methods of character reduction and weight determination are used to reduce the index and weight, establishing a large packaging information system. Experimental results show that the improved algorithm in this article produces more classification rules than the CAIM algorithm, because the discrete intervals created by the CAIM algorithm are relatively simple, but the classification rules are few, but less than the number of CAIM algorithms. Classification rules are generated by entropy-based sampling algorithms. This can make the classification rules simple and universal, and it is clear that the optimal sampling algorithm is more accurate than the CAIM algorithm.

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Section: Design Principle and Technology Of Packaging Big Data Inform...mentioning

confidence: 99%

Packaging Big Data Visualization Based on Computational Intelligence Information Design

Zhang

2022

Computational Intelligence and Neuroscience

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“…4 Youth and Digital Media lead: Denemark, The University of Western Australia. 5 Project website -http://wun.ac.uk/wun/research/view/ web-observatory-project significantly advances the project's longer-term ambition to bootstrap the worldwide development and adoption of Web Observatory infrastructure to the benefit of all research [2].…”

Section: Discussionmentioning

confidence: 99%

Worldwide Universities Network (WUN) Web Observatory

Price

Hall

Earl

et al. 2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. ABSTRACTThe ongoing growth in research data publication supports global intra-disciplinary and inter-disciplinary research collaboration but the current generation of archive-centric research data repositories do not address some of the key practical obstacles to research data sharing and re-use, specifically: discovering relevant data on a global scale is timeconsuming; sharing 'live' and streaming data is non-trivial; managing secure access to sensitive data is overly complicated; and, researchers are not guaranteed attribution for re-use of their own research data. These issues are keenly felt in an international network like the Worldwide Universities Network (WUN) as it seeks to address major global challenges. In this paper we outline the WUN Web Observatory project's plan to overcome these obstacles and, given that these obstacles are not unique to WUN, we also propose an ambitious, longer-term route to their solution at Web-scale by applying lessons from the Web itself.

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“…Data science follows a general process that includes data collection, data cleaning, data analysis, and modeling [9]. In data science, the data obtained is processed differently than in traditional approaches, so a new methodology must be applied to extract the necessary knowledge.…”

Section: Methodsmentioning

confidence: 99%

Application of Data Science to Discover Violence-Related Issues in Iraq

González¹,

Alférez²

2020

Preprint

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Data science has been satisfactorily used to discover social issues in several parts of the world. However, there is a lack of governmental open data to discover those issues in countries such as Iraq. This situation arises the following questions: how to apply data science principles to discover social issues despite the lack of open data in Iraq? How to use the available data to make predictions in places without data? Our contribution is the application of data science to open non-governmental big data from the Global Database of Events, Language, and Tone (GDELT) to discover particular violence-related social issues in Iraq. Specifically we applied the K-Nearest Neighbors, Näive Bayes, Decision Trees, and Logistic Regression classification algorithms to discover the following issues: refugees, humanitarian aid, violent protests, fights with artillery and tanks, and mass killings. The best results were obtained with the Decision Trees algorithm to discover areas with refugee crises and artillery fights. The accuracy for these two events is 0.7629. The precision to discover the locations of refugee crises is 0.76, the recall is 0.76, and the F1-score is 0.76. Also, our approach discovers the locations of artillery fights with a precision of 0.74, a recall of 0.75, and a F1-score of 0.75.

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The Role of Data Science in Web Science

Cited by 20 publications

References 10 publications

Packaging Big Data Visualization Based on Computational Intelligence Information Design

Packaging Big Data Visualization Based on Computational Intelligence Information Design

Worldwide Universities Network (WUN) Web Observatory

Application of Data Science to Discover Violence-Related Issues in Iraq

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