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 proceedingChapter

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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer-Verlag
Pages371-379
Number of pages9
ISBN (Print)9783319402581
DOIs
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9654

Keywords

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

Fingerprint

Dive into the research topics of 'Data mining, management and visualization in large scientific corpuses'. Together they form a unique fingerprint.

Cite this