Visualization analysis of big data research based on Citespace

In recent years, with the massive growth of data, the world today has entered the era of big data. Big data has brought tremendous value to all fields of today’s society, and it has also brought enormous challenges, which has attracted great attention from all walks of life. Analyze and forecast the research hotspots and future development trends in the field of big data, and understand the development changes and priorities in the field of big data research, which will play a significant role in promoting the development of social development and scientific research. In the era of big data, how to extract information from huge amounts of complex data and present complex information more clearly and clearly, the most effective way is to use visualization technology. The article uses the information visualization software Citespace to study the data related to big data in the Web of Science and CNKI database from 2008 to 2017 for 10 years, from macro to micro to the representative countries of the literature, keywords and co-cited documents. Through visualization analysis, the article clarifies the key research directions, key documents and hot spot frontiers in the field of big data research, forecasts the future development trends in this field, and compares the research situation at home and abroad, in order to provide readers and other researchers with certain reference and help.

[1]  Ronald C. Taylor An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics , 2010, BMC Bioinformatics.

[2]  Wang Shan,et al.  Architecting Big Data:Challenges,Studies and Forecasts , 2011 .

[3]  Yuanzhuo Wang,et al.  Network Big Data: Present and Future: Network Big Data: Present and Future , 2014 .

[4]  Francisco J. García-Peñalvo,et al.  Information retrieval methodology for aiding scientific database search , 2018, Soft Comput..

[5]  Wang Yuan,et al.  Network Big Data: Present and Future: Network Big Data: Present and Future , 2014 .

[6]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[7]  Wei Xiao-feng Visualization Research in Foreign Social Network Analysis Based on Mapping Knowledge Domain , 2011 .

[8]  Ying Gao,et al.  Generalized Pair-Counting Similarity Measures for Clustering and Cluster Ensembles , 2017, IEEE Access.

[9]  Wen-Wei Liao,et al.  Blog Interface Producing Mechanism in Learning Management System , 2014, J. Softw..

[10]  Wise Lab Review on the Application of CiteSpace at Home and Abroad , 2013 .

[11]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[12]  Kang Chen,et al.  Cloud Computing: System Instances and Current Research: Cloud Computing: System Instances and Current Research , 2010 .

[13]  Meng Xiaofeng and Ci Xiang,et al.  Big Data Management: Concepts,Techniques and Challenges , 2013 .

[14]  Chen Yue History and theory of mapping knowledge domains , 2008 .

[15]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[16]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[17]  Y. Ho,et al.  A bibliometric analysis of solid waste research during the period 1993-2008. , 2010, Waste Management.

[18]  Tianyong Hao,et al.  A Data-Driven Approach for Discovering the Recent Research Status of Diabetes in China , 2017, HIS.

[19]  Zhengming Ma,et al.  Regularized constraint subspace based method for image set classification , 2018, Pattern Recognit..

[20]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[21]  Jun Yan,et al.  A bibliometric analysis of text mining in medical research , 2018, Soft Computing.

[22]  Xizhao Wang,et al.  Preface for the special issue: soft computing in machine learning and cybernetics in the journal Soft Computing , 2006, Soft Comput..

[23]  Alexander M Shneider Four stages of a scientific discipline; four types of scientist. , 2009, Trends in biochemical sciences.

[24]  Cheng Xueqi,et al.  Survey on Big Data System and Analytic Technology , 2014 .

[25]  Ying Gao,et al.  Patch-Based Principal Covariance Discriminative Learning for Image Set Classification , 2017, IEEE Access.

[26]  B. Sarojini,et al.  Survey on Big Data and Cloud Computing , 2017, 2017 World Congress on Computing and Communication Technologies (WCCCT).

[27]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[28]  Xuan Zhou,et al.  Architecting Big Data: Challenges, Studies and Forecasts: Architecting Big Data: Challenges, Studies and Forecasts , 2011 .

[29]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[30]  Shi Ying A Survey of Query Techniques in Cloud Data Management Systems , 2013 .

[31]  Reza Zafarani,et al.  The good, the bad, and the ugly: uncovering novel research opportunities in social media mining , 2016, International Journal of Data Science and Analytics.

[32]  Chunxia Zhang,et al.  Discovering the Recent Research in Natural Language Processing Field Based on a Statistical Approach , 2017, SETE@ICWL.

[33]  LiYang,et al.  Knowledge Graph Construction Techniques , 2016 .

[34]  Qinghua Zheng,et al.  Simple to Complex Cross-modal Learning to Rank , 2017, Comput. Vis. Image Underst..

[35]  Wei Li,et al.  Bibliometric analysis of global environmental assessment research in a 20-year period , 2015 .

[36]  C. L. Philip Chen,et al.  Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..

[37]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[38]  D J PRICE,et al.  NETWORKS OF SCIENTIFIC PAPERS. , 1965, Science.

[39]  Xiao-Feng Meng,et al.  A Survey of Query Techniques in Cloud Data Management Systems: A Survey of Query Techniques in Cloud Data Management Systems , 2014 .

[40]  Zhihui Li,et al.  Beyond Trace Ratio: Weighted Harmonic Mean of Trace Ratios for Multiclass Discriminant Analysis , 2017, IEEE Transactions on Knowledge and Data Engineering.

[41]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

[42]  Yunhao Liu,et al.  Big Data: A Survey , 2014, Mob. Networks Appl..