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228 Journal of Library Science in China, Vol. 8, 2016

discovery. Any person at any time and any place can get its ubiquitous services from an Internet
access device. It accepts a request from an Internet agent and provides a service to a network
anonymous reader through the agent. The result the reader gets is a “knowledge module” form
which may be the text,video,audio,experimental process,network connectivity,software systems
and/or abstract data format. Because of the huge amount of contents,the operation of its collection
and renovation needs to be automated. Raw data from different sources and different formats will
be integrated and unified in order to be shareable and open,but all the contents are dispersed in a
mixed cloud and the big data library system also runs in the cloud. The support system of the big-
data-base is Big Table or H Base. It may be a resource polymerization of all the libraries in a field
or a nation,and may even be the convergence of all the libraries in the world. Looking further into
the future,a library should not wait for readers to consult the literature but not deliver accurately
the desired content to users.Instead, it should actively participate in the global brain program and
use its own big data inventory advantage to provide adequate knowledge resources for the brain
reasoning machine. The Baidu index gives only seven visual results made from China’s domestic
data,and the next step of our research is to use the global data.

Research on visualization analysis method of discipline topics evolution from
the perspective of multi-dimensions:A case study of the big data in the field of
Library and Information Science in China
①a *
LIU Ziqiang,WANG Xiaoyue & BAI Rujiang
Detection and identification of the evolution of research topics in a discipline has important
significance for researchers to grasp its research status and development trend. Visual analysis
can show the relationship between themes based on topics recognition,help users to enhance
their perception and cognition,and to find useful information quickly in a field on the research
status,research hotspots and development trends,and to digest,understand and effectively analyze
vast amounts of information. However,discipline topics evolution is a complex process and there
are many variables,such as the intensity,structure and content of topics. The single dimension
visualization analysis causes the information overload,leading to three problems:perceptive
limitations,cognitive limitations,and performance limitations.
This paper presents a visualization analysis method of discipline topics evolution from a
multidimensional perspective:using the artificial annotation method to make semantic role
classification of keywords,using Fast Unfolding algorithm recognition with the semantic features
to identify the topics；using cosine similarity to calculate the formula of similarity between topics
evolution；constructing evolution analysis model of multidimensional discipline topics,and
designing three innovative scientific knowledge map by using JavaScript and Web front-end
* Correspondence should be addressed to WANG Xiaoyue,Email:sdutcspace@163.com,ORCID:0000-0002-7100-7758