Data mining, management and visualization in large scientific corpuses

Hui Wei; Shaopeng Wu; Youbing Zhao; Zhikun Deng; Nikolaos Ersotelos; Farzad Parvinzamir; Baoquan Liu; Enjie Liu; Feng Dong

doi:10.1007/978-3-319-40259-8_32

Data mining, management and visualization in large scientific corpuses

Hui Wei
, Shaopeng Wu
, Youbing Zhao
, Zhikun Deng
, Nikolaos Ersotelos
, Farzad Parvinzamir
, Baoquan Liu
, Enjie Liu
, Feng Dong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	E-Learning and Games 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected Papers
Publisher	Springer
Pages	371-379
Volume	9654
ISBN (Print)	9783319402581
DOIs	https://doi.org/10.1007/978-3-319-40259-8_32
Publication status	Published - 31 Dec 2016
Event	International Conference on Technologies for E-Learning and Digital Entertainment: E-Learning and Games 10th International Conference - Hangzhou Duration: 14 Apr 2016 → 16 Apr 2016

Conference

Conference	International Conference on Technologies for E-Learning and Digital Entertainment: E-Learning and Games 10th International Conference
City	Hangzhou
Period	14/04/16 → 16/04/16
Other	International Conference on Technologies for E-Learning and Digital Entertainment: E-Learning and Games 10th International Conference (14/04/2016-16/04/2016, Hangzhou)

Keywords

Data management
Distributed storage
Document repository
Elasticsearch
Graph database
NoSql
Visualization
text mining

Access to Document

10.1007/978-3-319-40259-8_32

https://link.springer.com/chapter/10.1007%252F978-3-319-40259-8_32

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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. In E-Learning and Games 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected Papers (Vol. 9654, pp. 371-379). Springer. https://doi.org/10.1007/978-3-319-40259-8_32

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title = "Data mining, management and visualization in large scientific corpuses",

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.",

keywords = "Data management, Distributed storage, Document repository, Elasticsearch, Graph database, NoSql, Visualization, text mining",

author = "Hui Wei and Shaopeng Wu and Youbing Zhao and Zhikun Deng and Nikolaos Ersotelos and Farzad Parvinzamir and Baoquan Liu and Enjie Liu and Feng Dong",

year = "2016",

month = dec,

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doi = "10.1007/978-3-319-40259-8\_32",

language = "English",

isbn = "9783319402581",

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booktitle = "E-Learning and Games 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected Papers",

publisher = "Springer",

address = "Germany",

note = "International Conference on Technologies for E-Learning and Digital Entertainment: E-Learning and Games 10th International Conference ; Conference date: 14-04-2016 Through 16-04-2016",

}

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. in E-Learning and Games 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected Papers. vol. 9654, Springer, pp. 371-379, International Conference on Technologies for E-Learning and Digital Entertainment: E-Learning and Games 10th International Conference, Hangzhou, 14/04/16. https://doi.org/10.1007/978-3-319-40259-8_32

Data mining, management and visualization in large scientific corpuses. / Wei, Hui; Wu, Shaopeng; Zhao, Youbing et al.
E-Learning and Games 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected Papers. Vol. 9654 Springer, 2016. p. 371-379.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data mining, management and visualization in large scientific corpuses

AU - Wei, Hui

AU - Wu, Shaopeng

AU - Zhao, Youbing

AU - Deng, Zhikun

AU - Ersotelos, Nikolaos

AU - Parvinzamir, Farzad

AU - Liu, Baoquan

AU - Liu, Enjie

AU - Dong, Feng

PY - 2016/12/31

Y1 - 2016/12/31

N2 - 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.

AB - 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.

KW - Data management

KW - Distributed storage

KW - Document repository

KW - Elasticsearch

KW - Graph database

KW - NoSql

KW - Visualization

KW - text mining

U2 - 10.1007/978-3-319-40259-8_32

DO - 10.1007/978-3-319-40259-8_32

M3 - Conference contribution

SN - 9783319402581

VL - 9654

SP - 371

EP - 379

BT - E-Learning and Games 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected Papers

PB - Springer

T2 - International Conference on Technologies for E-Learning and Digital Entertainment: E-Learning and Games 10th International Conference

Y2 - 14 April 2016 through 16 April 2016

ER -

Data mining, management and visualization in large scientific corpuses

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Keywords

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