Ten challenges in modeling bibliographic data for bibliometric analysis

被引:23
|
作者
Ferrara, Alfio [2 ]
Salini, Silvia [1 ]
机构
[1] Univ Milan, Dipartimento Sci Econ Aziendali & Stat, Milan, Italy
[2] Univ Milan, Dipartimento Informat & Comunicaz, Milan, Italy
关键词
Dimensional data modeling; Multivariate statistics; Multidimensional data analysis; Topics models; LEAGUE TABLES; SCIENCE; CLASSIFICATION; RANKINGS; SCHOLAR; SCOPUS; WEB;
D O I
10.1007/s11192-012-0810-x
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The complexity and variety of bibliographic data is growing, and efforts to define new methodologies and techniques for bibliometric analysis are intensifying. In this complex scenario, one of the most crucial issues is the quality of data and the capability of bibliometric analysis to cope with multiple data dimensions. Although the problem of enforcing a multidimensional approach to the analysis and management of bibliographic data is not new, a reference design pattern and a specific conceptual model for multidimensional analysis of bibliographic data are still missing. In this paper, we discuss ten of the most relevant challenges for bibliometric analysis when dealing with multidimensional data, and we propose a reference data model that, according to different goals, can help analysis designers and bibliographic experts in working with large collections of bibliographic data.
引用
收藏
页码:765 / 785
页数:21
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