Predicting Researchers' Future Activities Using Visualization System for Co-authorship Networks
ABSTRACT This paper proposes a visualization system for getting insight into future research activities from co-authorship networks. A bibliographic network such as a co-authorship network and a citation network is important information for researchers when doing a research survey. In particular, there are many requests on research survey that relate with researchers' future activities, such as identification of remarkable of researchers including growing researchers and supervisors. Although a citation network has received many attentions from researchers, it is not suitable for such surveys because it reflects researchers' past activities. Since collaboration of researchers is essential for researchers' activities, co-authorship network is suitable for predicting future activities. In order to get insights into future research activities by discriminating growing research areas from grown-up areas, the proposed visualization system provides the function for identifying research areas and that for identifying time variation of both network structure and keyword distribution. As a basis for getting insights into future research activities, this paper focuses on the task of discriminating growing researchers from supervisors. The effectiveness of the proposed system is evaluated through the detailed analysis of two participants' analyzing process of InfoVis 2004 Contest dataset.
Conference Paper: Application of monitoring support visualization to bug tracking systems[Show abstract] [Hide abstract]
ABSTRACT: This paper proposes to apply information visualization technologies to the support of monitoring bug update information sent from multiple bug tracking systems. Bug update information managed by bug tracking systems (BTS) is one of text stream data, which continuously generates new data. Therefore, it is difficult for users to watch it all the time. In other words, the task of monitoring stream data inevitably involves breaks of the task, which would lose the context of monitoring. However, to the best of our knowledge, interaction design when involving breaks has not been fully studied yet. The proposed system visualizes the dynamic relationship between bugs with animation, and helps a user grasping the context of monitoring by highlighting updated bugs and the replay of animation for part of the last monitoring time. The effectiveness of the system is evaluated through experiments with test participants. Recent growth of the Web has brought us various kinds of text stream data, such as bulletin board systems (BBS), blogs, and social networking services (SNS). As such data is expected to be important resources for human support robots, this paper would contribute to interaction design of such robots.Industrial Electronics (ISIE), 2013 IEEE International Symposium on; 01/2013
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ABSTRACT: The Brazilian Lattes Platform is an important academic/resume dataset that registers all of the academic activity of researchers associated with different major knowledge areas. Currently, the activity of over a million researchers has been registered in this dataset. The academic information collected in this dataset is used to evaluate, analyze, and document the scientific production of research groups. Information about the interactions between Brazilian researchers in the form of co-authorships, however, has not been analyzed. In this paper we identified and characterized Brazilian academic co-authorship networks of researchers registered in the Lattes Platform, using topological properties of graphs. For this purpose, we explored (i) strategies to develop a very large Lattes curricula dataset, (ii) an algorithm for identifying automatic co-authorships based on bibliographic information, and (iii) topological metrics to investigate interactions among researchers. The aim of our study was to characterize co-authorship networks to gain an in-depth understanding of the network structures and dynamics (social behavior) among researchers in all available Brazilian major knowledge areas. In this study, we evaluated information from a total of 1,131,912 researchers associated with the 8 major Brazilian knowledge areas: Agricultural Sciences; Biological Sciences; Exact and Earth Sciences; Humanities; Applied Social Sciences; Health Sciences; Engineering; and Linguistics, Letters and Arts.Journal of the American Society for Information Science and Technology 07/2014; DOI:10.1002/asi.23010 · 2.01 Impact Factor