Database Tomography Applied to an Aircraft Science and Technology Investment Strategy

Kostoff, Ronald N.; Green, Kenneth A.; Toothman, Darrell Ray; Humenik, James A.

doi:10.2514/2.2659

Cited by 43 publications

(37 citation statements)

References 20 publications

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“…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.…”

Section: Iii-c Relevance Feedback and Query Expansionmentioning

confidence: 99%

“…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.…”

Section: Iii-f Application Of Simulated Nucleation For Information Rmentioning

confidence: 99%

“…Relation of Query Structure to Study Objectives, Construction Philosophy, and Database Selected Specifically, one of the key findings from a FY98 ONR TDM pilot program was that, in general, a separate query had to be developed for each database examined (Kostoff and Geisler, 1999c;Kostoff et al, 2000c). Each database accesses a particular culture, with its unique language and unique types of documentation and expression.…”

Section: Iv-c Generation Of Query Development Strategymentioning

confidence: 99%

“…For example, one of the FY98 pilot program studies focused on the R&D of the aircraft platform (Kostoff et al, 2000c). The query philosophy was to start with the generic term AIRCRAFT, then add terms that would expand the numbers of aircraft R&D records (mainly journal paper Abstracts) retrieved and would eliminate records not relevant to aircraft R&D. Two databases were queried, the Science Citation Index (SCI-a database accessing basic research records) and the Engineering Compendex (EC-a database accessing applied research and technology records).…”

Section: Iv-c Generation Of Query Development Strategymentioning

confidence: 99%

“…The text fields (Keywords, Titles, Abstracts) are used by their originators for different purposes, and contain different levels of information content and detail (Kostoff et al, 2000c. The query and other computational linguistics results could provide a different picture of the overall discipline studied based on which field was examined.…”

Section: Iv-c-ii Relation Of Query Structure To Database Fields Selementioning

confidence: 99%

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Section: Iii-c Relevance Feedback and Query Expansionmentioning

confidence: 99%

Section: Iii-f Application Of Simulated Nucleation For Information Rmentioning

confidence: 99%

Section: Iv-c Generation Of Query Development Strategymentioning

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Section: Iv-c Generation Of Query Development Strategymentioning

confidence: 99%

Section: Iv-c-ii Relation Of Query Structure To Database Fields Selementioning

confidence: 99%

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Identifying the users and impact of research is important for research performers, managers, evaluators, and sponsors. It is important to know whether the audience reached is the audience desired. It is useful to understand the technical characteristics of the other research/development/applications impacted by the originating research, and to understand other characteristics (names, organizations, countries) of the users impacted by the research. Because of the many indirect pathways through which fundamental research can impact applications, identifying the user audience and the research impacts can be very complex and time consuming. The purpose of this article is to describe a novel approach for identifying the pathways through which research can impact other research, technology development, and applications, and to identify the technical and infrastructure characteristics of the user population. A novel literature-based approach was developed to identify the user community and its characteristics. The research performed is characterized by one or more articles accessed by the Science Citation Index (SCI) database, beccause the SCI's citation-based structure enables the capability to perform citation studies easily.The user community is characterized by the articles in the SCI that cite the original research articles, and that cite the succeeding generations of these articles as well. Text mining is performed on the citing articles to identify the technical areas impacted by the research, the relationships among these technical areas, and relationships among the technical areas and the infrastructure (authors, journals, organizations). A key component of text mining, concept clustering, was used to provide both a taxonomy of the citing articles' technical themes and further technical insights based on theme relationships arising from the grouping process. Bibliometrics is performed on the citing articles to profile the user characteristics. Citation Mining, this integration of citation bibliometrics and text mining, is applied to the 307 first generation citing articles of a fundamental physics article on the dynamics of vibrating sand-piles. Most of the 307 citing articles were basic research whose main themes were aligned with those of the cited article. However, about 20% of the citing articles were research or development in other disciplines, or development within the same discipline. The text mining alone identified the intradiscipline applications and extradiscipline impacts and applications; this was confirmed by detailed reading of the 307 abstracts. The combination of citation bibliometrics and text mining provides a synergy unavailable with each approach taken independently. Furthermore, text mining is a REQUIREMENT for a feasible comprehensive research impact determination. The integrated multigeneration citation analysis required for broad research impact determination of highly cited articles will produce thousands or tens or hundreds of thousands of citing article Abstracts. Text mining allows...

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Factor matrix text filtering and clustering

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. Additionally, the determination of whether a word/phrase is trivial is context-dependent. Our objective in the present article is to demonstrate a context-dependent trivial word filter to improve clustering quality. Factor analysis was used as a context-dependent trivial word filter for subsequent term clustering. Medline records for Raynaud's Phenomenon were used as the database, and words were extracted from the record abstracts. A factor matrix of these words was generated, and the words that had low factor loadings across all factors were identified, and eliminated. The remaining words, which had high factor loading values for at least one factor and therefore were influential in determining the theme of that factor, were input to the clustering algorithm. Both quantitative and qualitative analyses were used to show that factor matrix filtering leads to higher quality clusters and subsequent taxonomies.

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Database Tomography Applied to an Aircraft Science and Technology Investment Strategy

Cited by 43 publications

References 20 publications

Systematic acceleration of radical discovery and innovation in science and technology

Systematic acceleration of radical discovery and innovation in science and technology

Citation mining: Integrating text mining and bibliometrics for research user profiling

Factor matrix text filtering and clustering

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