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    Data mining, management and visualization in large scientific corpuses

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    Authors
    Wei, Hui
    Wu, Shaopeng
    Zhao, Youbing
    Deng, Zhikun
    Ersotelos, Nikolaos
    Parvinzamir, Farzad
    Liu, Baoquan
    Liu, Enjie
    Dong, Feng
    Affiliation
    University of Bedfordshire
    Issue Date
    2016-12-31
    Subjects
    distributed storage
    text mining
    graph database
    elasticsearch
    document repository
    data management
    visualization
    NoSql
    
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    Other Titles
    E-Learning and Games 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected Papers
    Abstract
    Organizing scientific papers helps efficiently derive meaningful insights of the published scientific resources, enables researchers grasp rapid technological change and hence assists new scientific discovery. In this paper, we experiment text mining and data management of scientific publications for collecting and presenting useful information to support research. For efficient data management and fast information retrieval, four data storages are employed: a semantic repository, an index and search repository, a document repository and a graph repository, taking full advantage of their features and strength. The results show that the combination of these four repositories can effectively store and index the publication data with reliability and efficiency and hence supply meaningful information to support scientific research.
    Citation
    Wei H, Wu S, Zhao Y, Deng Z, Ersotelos N, Parvinzamir F, Liu B, Liu E, Dong F (2016) 'Data mining, management and visualization in large scientific corpuses', International Conference on Technologies for E-Learning and Digital Entertainment: E-Learning and Games 10th International Conference - Hangzhou, Springer Verlag.
    Publisher
    Springer Verlag
    URI
    http://hdl.handle.net/10547/624195
    DOI
    10.1007/978-3-319-40259-8_32
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-319-40259-8_32
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9783319402581
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-40259-8_32
    Scopus Count
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    Computing

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