With or without h-index? Comparing aggregates of rankings based on seven popular bibliometric indicators
This paper introduces a systematic technology trend monitoring (TTM) methodology based on an analysis of bibliometric data. Among the key premises for developing a methodology are: (1) the increasing number of data sources addressing different phases of the STI development, and thus requiring a more holistic and integrated analysis; (2) the need for more customized clustering approaches particularly for the purpose of identifying trends; and (3) augmenting the policy impact of trends through gathering future-oriented intelligence on emerging developments and potential disruptive changes. Thus, the TTM methodology developed combines and jointly analyzes different datasets to gain intelligence to cover different phases of the technological evolution starting from the ‘emergence’ of a technology towards ‘supporting’ and ‘solution’ applications and more ‘practical’ business and market-oriented uses. Furthermore, the study presents a new algorithm for data clustering in order to overcome the weaknesses of readily available clusterization tools for the purpose of identifying technology trends. The present study places the TTM activities into a wider policy context to make use of the outcomes for the purpose of Science, Technology and Innovation policy formulation, and R&D strategy making processes. The methodology developed is demonstrated in the domain of “semantic technologies”.
Abstract Clustering cities based on their socio-economic development in long time period is an important issue and may be used in many ways, e.g., in strategic regional planning. In this paper we continue our recent study where cumulative attribute for each year replaces nine other attributes, called ’vector of dynamics’. In our previous paper some original ranking method was proposed. Using the same data set, here we try out some classical clustering models such as Minimum sum of squares and Harmonic means clustering. Results for the two last models are obtained using Variable neighborhood search based heuristics. A comparative study among old and new results on 120 Russian large cities are provided and analyzed.