IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v30y2005i5p709-730.html
   My bibliography  Save this article

Power source roadmaps using bibliometrics and database tomography

Author

Listed:
  • Kostoff, R.N.
  • Tshiteya, R.
  • Pfeil, K.M.
  • Humenik, J.A.
  • Karypis, G.

Abstract

Database Tomography (DT) is a textual database analysis system consisting of two major components: (1) algorithms for extracting multi-word phrase frequencies and phrase proximities (physical closeness of the multi-word 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 Power Sources database derived from the Science Citation Index. Phrase frequency analysis by the technical domain experts provided the pervasive technical themes of the Power Sources database, and the phrase proximity analysis provided the relationships among the pervasive technical themes. Bibliometric analysis of the Power Sources literature supplemented the DT results with author/journal/institution/country publication and citation data.

Suggested Citation

  • Kostoff, R.N. & Tshiteya, R. & Pfeil, K.M. & Humenik, J.A. & Karypis, G., 2005. "Power source roadmaps using bibliometrics and database tomography," Energy, Elsevier, vol. 30(5), pages 709-730.
  • Handle: RePEc:eee:energy:v:30:y:2005:i:5:p:709-730
    DOI: 10.1016/j.energy.2004.04.058
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544204002531
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2004.04.058?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ronald N. Kostoff & J. Antonio del Río & James A. Humenik & Esther Ofilia García & Ana María Ramírez, 2001. "Citation mining: Integrating text mining and bibliometrics for research user profiling," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(13), pages 1148-1156.
    2. Michael D. Gordon & Susan Dumais, 1998. "Using latent semantic indexing for literature based discovery," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(8), pages 674-685.
    3. R. N. Kostoff & Henry J. Eberhart & Darrell Ray Toothman, 1999. "Hypersonic and supersonic flow roadmaps using bibliometrics and database tomography," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(5), pages 427-447.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kiriyama, Eriko & Kajikawa, Yuya, 2014. "A multilayered analysis of energy security research and the energy supply process," Applied Energy, Elsevier, vol. 123(C), pages 415-423.
    2. Kiriyama, Eriko & Kajikawa, Yuya & Fujita, Katsuhide & Iwata, Shuichi, 2013. "A lead for transvaluation of global nuclear energy research and funded projects in Japan," Applied Energy, Elsevier, vol. 109(C), pages 145-153.
    3. Rizzi, Francesco & van Eck, Nees Jan & Frey, Marco, 2014. "The production of scientific knowledge on renewable energies: Worldwide trends, dynamics and challenges and implications for management," Renewable Energy, Elsevier, vol. 62(C), pages 657-671.
    4. Ogawa, Takaya & Kajikawa, Yuya, 2017. "Generating novel research ideas using computational intelligence: A case study involving fuel cells and ammonia synthesis," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 41-47.
    5. Hsin-Ning Su & Pei-Chun Lee, 2010. "Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 65-79, October.
    6. Su, Hsin-Ning & Lee, Pei-Chun, 2012. "Framing the structure of global open innovation research," Journal of Informetrics, Elsevier, vol. 6(2), pages 202-216.
    7. Ogawa, Takaya & Kajikawa, Yuya, 2015. "Assessing the industrial opportunity of academic research with patent relatedness: A case study on polymer electrolyte fuel cells," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 469-475.
    8. Martin, Hilary & Daim, Tugrul U., 2012. "Technology roadmap development process (TRDP) for the service sector: A conceptual framework," Technology in Society, Elsevier, vol. 34(1), pages 94-105.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van Eck, N.J.P. & Waltman, L., 2009. "How to Normalize Co-Occurrence Data? An Analysis of Some Well-Known Similarity Measures," ERIM Report Series Research in Management ERS-2009-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
    3. Ronald Kostoff & Raymond Koytcheff & Clifford Lau, 2008. "Structure of the nanoscience and nanotechnology applications literature," The Journal of Technology Transfer, Springer, vol. 33(5), pages 472-484, October.
    4. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    5. Jose M. Vicente-Gomila, 2014. "The contribution of syntactic–semantic approach to the search for complementary literatures for scientific or technical discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 659-673, September.
    6. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.
    7. Andrej Kastrin & Dimitar Hristovski, 2021. "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1415-1451, February.
    8. Choudhury, Nazim & Faisal, Fahim & Khushi, Matloob, 2020. "Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-based Discovery Prediction," Journal of Informetrics, Elsevier, vol. 14(3).
    9. Xu, Guannan & Wu, Yuchen & Minshall, Tim & Zhou, Yuan, 2018. "Exploring innovation ecosystems across science, technology, and business: A case of 3D printing in China," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 208-221.
    10. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    11. José Luis Ruiz-Real & Juan Uribe-Toril & Jaime De Pablo Valenciano & Juan Carlos Gázquez-Abad, 2018. "Worldwide Research on Circular Economy and Environment: A Bibliometric Analysis," IJERPH, MDPI, vol. 15(12), pages 1-14, November.
    12. Oscar S. Santillán & Karla G. Cedano, 2023. "Energy Literacy: A Systematic Review of the Scientific Literature," Energies, MDPI, vol. 16(21), pages 1-19, October.
    13. Justin Mower & Trevor Cohen & Devika Subramanian, 2020. "Complementing Observational Signals with Literature-Derived Distributed Representations for Post-Marketing Drug Surveillance," Drug Safety, Springer, vol. 43(1), pages 67-77, January.
    14. Michael Gowanlock & Rich Gazan, 2013. "Assessing researcher interdisciplinarity: a case study of the University of Hawaii NASA Astrobiology Institute," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 133-161, January.
    15. Johannes Stegmann & Guenter Grohmann, 2003. "Hypothesis generation guided by co-word clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 111-135, January.
    16. Oscar S. Santillán & Karla G. Cedano & Manuel Martínez, 2020. "Analysis of Energy Poverty in 7 Latin American Countries Using Multidimensional Energy Poverty Index," Energies, MDPI, vol. 13(7), pages 1-19, April.
    17. Alan L. Porter & Alex S. Cohen & J. David Roessner & Marty Perreault, 2007. "Measuring researcher interdisciplinarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(1), pages 117-147, July.
    18. Ronald N. Kostoff & Stephen A. Morse, 2011. "Structure and infrastructure of infectious agent research literature: SARS," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 195-209, January.
    19. Benito-Santos, Alejandro & Theron, Roberto, 2019. "Cross-domain Visual Exploration of Academic Corpora via the Latent Meaning of User-authored Keywords," OSF Preprints h29qv, Center for Open Science.
    20. Chihmao Hsieh, 2011. "Explicitly searching for useful inventions: dynamic relatedness and the costs of connecting versus synthesizing," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(2), pages 381-404, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:30:y:2005:i:5:p:709-730. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.