Database tomography is an information extraction and analysis system which operates on textual databases. Its primary use to date has been to identify pervasive technical thrusts and themes, and the interrelationships among these themes and sub-themes, which are intrinsic to large textual databases. Its two main algorithmic components are multiword phrase frequency analysis and phrase proximity analysis. This paper shows how database tomography can be used to enhance information retrieval from large textual databases through the newly developed process of simulated nucleation. The principles of simulated nucleation are presented, and the advantages for information retrieval are delineated. An application is described of developing, from Science Citation Index and Engineering Compendex, a database of journal articles focused on near-Earth space science and technology.

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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.

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