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 › Chapter

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	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer-Verlag
Pages	371-379
Number of pages	9
ISBN (Print)	9783319402581
DOIs	https://doi.org/10.1007/978-3-319-40259-8_32
Publication status	Published - 2016
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9654

Keywords

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

Access to Document

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

http://www.mendeley.com/research/data-mining-management-visualization-large-scientific-corpuses

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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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 371-379). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9654). Springer-Verlag. https://doi.org/10.1007/978-3-319-40259-8_32

Wei, Hui ; Wu, Shaopeng ; Zhao, Youbing ; Deng, Zhikun ; Ersotelos, Nikolaos ; Parvinzamir, Farzad ; Liu, Baoquan ; Liu, Enjie ; Dong, Feng. / Data mining, management and visualization in large scientific corpuses. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer-Verlag, 2016. pp. 371-379 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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, Text mining, Visualization",

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",

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

language = "English",

isbn = "9783319402581",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9654, Springer-Verlag, pp. 371-379. 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; Deng, Zhikun; Ersotelos, Nikolaos; Parvinzamir, Farzad; Liu, Baoquan; Liu, Enjie; Dong, Feng.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer-Verlag, 2016. p. 371-379 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9654).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

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

Y1 - 2016

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 - Text mining

KW - Visualization

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

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

M3 - Chapter

SN - 9783319402581

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 371

EP - 379

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer-Verlag

ER -

Wei H, Wu S, Zhao Y, Deng Z, Ersotelos N, Parvinzamir F et al. Data mining, management and visualization in large scientific corpuses. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer-Verlag. 2016. p. 371-379. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-40259-8_32