Data mining, management and visualization in large scientific corpuses
Authors
Wei, HuiWu, Shaopeng
Zhao, Youbing
Deng, Zhikun
Ersotelos, Nikolaos
Parvinzamir, Farzad
Liu, Baoquan
Liu, Enjie
Dong, Feng
Affiliation
University of BedfordshireIssue Date
2016-12-31Subjects
distributed storagetext mining
graph database
elasticsearch
document repository
data management
visualization
NoSql
Metadata
Show full item recordOther Titles
E-Learning and Games 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected PapersAbstract
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 VerlagAdditional Links
https://link.springer.com/chapter/10.1007%2F978-3-319-40259-8_32Type
Conference papers, meetings and proceedingsLanguage
enISBN
9783319402581ae974a485f413a2113503eed53cd6c53
10.1007/978-3-319-40259-8_32