Hypersonic and supersonic flow roadmaps using bibliometrics and database tomography

Abstract

Database Tomography (DT) is a textual database analysis system consisting of two major components: 1) Algorithms for extracting multiword phrase frequencies and phrase proximities (physical closeness of the multiword technical phrases) from any type of large textual database, to augment 2) interpretative capabilities of the expert human analyst. DT was used to derive technical intelligence from a hypersonic/supersonic flow (HSF) database derived from the Science Citation Index and the Engineering Compendex. Phrase frequency analysis by the technical domain expert provided the pervasive technical themes of the HSF database, and the phrase proximity analysis provided the relationships among the pervasive technical themes. Bibliometric analysis of the HSF literature supplemented the DT results with author/ journal/institution publication and citation data. Comparisons of HSF results with past analyses of similarly structured near-earth space and Chemistry databases are made. One important finding is that many of the normalized bibliometric distribution functions are extremely consistent across these diverse technical domains.

Systematic acceleration of radical discovery and innovation in science and technology

“…For databases of non-journal technical articles (Kostoff, 1991b(Kostoff, , 1993b(Kostoff, , 1993c(Kostoff, , 1994b(Kostoff, , 1994c) the final results have been identification of the pervasive technical themes of the database, the relationship among these themes, and the relationship of supporting sub-thrust areas (both high and low frequency) to the high-frequency themes. For the more recent studies in which the databases are journal article abstracts and associated bibliometric information (authors, journals, addresses, etc), the final results have also included relationships among the technical themes and authors, journals, institutions, etc (Kostoff et al, 1997b(Kostoff et al, , 1997c(Kostoff et al, , 1998a(Kostoff et al, , 1999a(Kostoff et al, , 2000a(Kostoff et al, , 2000c(Kostoff et al, , 2000g, 2001b(Kostoff et al, , 2001g, 2002a(Kostoff et al, , 2003c(Kostoff et al, , 2003d(Kostoff et al, , 2003l, 2003n, 2003p, 2003s, 2004a(Kostoff et al, , 2004c(Kostoff et al, , 2004d(Kostoff et al, , 2004f, 2004h, 2004i, 2004j, 2004l, 2004m, 2004n, 2004o, 2005a(Kostoff et al, , 2005b.…”

“…The interested reader is strongly urged to read this document, in order to obtain a detailed understanding of the operation of Simulated Nucleation. Since that time, Simulated Nucleation has been used in a number of published data mining studies in different topical areas (Kostoff et al, 1997b(Kostoff et al, , 1997c(Kostoff et al, , 1998a(Kostoff et al, , 1999a(Kostoff et al, , 2000a(Kostoff et al, , 2000c(Kostoff et al, , 2000g, 2001b(Kostoff et al, , 2001g, 2002a(Kostoff et al, , 2003c(Kostoff et al, , 2003d(Kostoff et al, , 2003l, 2003n, 2003p, 2003s, 2004a(Kostoff et al, , 2004c(Kostoff et al, , 2004d(Kostoff et al, , 2004f, 2004h, 2004i, 2004j, 2004l, 2004m, 2004n, 2004o, 2005a(Kostoff et al, , 2005b, and in shorter nonpublished efforts as well. Much deeper insights about the operation and benefits of Simulated Nucleation have been obtained, and a number of algorithmic upgrades have been made to enhance the power and efficiency of Simulated Nucleation.…”

Science and Technology Text Mining: Electric Power Sources

“…All are from universities. This prolific author country distribution differs radically from any in previous studies [27][28][29][30][31][32], with the high concentration from India. These prolific author countries in previous text mining studies tended to be dominated by Northern America countries (United States and Canada), the most developed Western European nations (UK, Germany, France, Italy), and the major oriental Asian countries (Japan, China, South korea).…”

“…It differs from the previous published papers in this category [27][28][29][30][31][32] in four respects. First, the topical domain (power sources) is completely different.…”

Science and Technology Text Mining: Strategic Management and Implementation in Government Organization

“…Information retrieval/ text mining efforts (e.g., Kostoff et al, 1999aKostoff et al, , 2000a have shown that an iterative process that incorporates a broad scope of 'relevant' disciplines to the target discipline requires the participation of a technical domain expert(s) and a computational linguistics expert(s) (or at least a documented procedure using computational linguistics tools). There is substantial judgement and interpretation required by at least one expert at each iterative step, and this effort directly translates into significant resource expenditures.…”