Art. 01 – Vol. 24 – No. 3 – 2014

Support system for bibliomining decision

Cornel Lepădatu
cornel_lepadatu@biblacad.ro

Romanian Academy

Abstract: Decision Support Systems provide knowledge and knowledge processing capacity essential for referral decision situations and decision-making, improving decision-making and decision-making results and relaxing the cognitive, temporal, spatial and economic limits of the decision-makers. Libraries and librarians support in decisions-making varied in time from a passive, traditional collections of books and journals, to some highly active, decision assistants. Digital libraries have provided new insights for corporate decision support systems. Data mining techniques have become crucial for the management, organization and dissemination of information to the right users at the right time. Bibliomining provides the opportunity through a single data warehouse to compile knowledge on the interconnections between different social networks, the community of authors and the community made up of the library and its users. Library decision support system tends to become a naturally important actor in the supply of knowledge to companies decision support systems. The paper presents some results obtained from a formalized approach to build a library decision support system.

Keywords: Bibliometrics, Library and Information Science, Bibliographic Record, Data Warehousing, Data Mining and Knowledge Discovery, Decision Support System.

View full article

REFERENCES

  1. BA, S.; HINKKANEN, A.; WHINSTON, A. B.: Digital Library as a Foundation for Decision Support Systems. In Proceedings of the First Annual Conference on the Theory and Practice of Digital Libraries, College Station, TX, Ed. J. Schnase, J. Leggett, R. Furuta, and T. Metcalfe, 1994, pp. 170 – 177.
  2. BNF – Bibliothèque nationale de France: Fonctionnalités requises des notices bibliographiques: rapport final. Trad. de: „Functional requirements for bibliographic records : Final Report” – 2e édition française, Paris, BNF, 2012, 100 p.
  3. BURSTEIN, F.; HOLSAPPLE, C. W. (Eds): Handbook on Decision Support Systems 1 : Basic Themes. Internat. Handbooks on Information Systems, Springer-Verlag, 2008, 854 p.
  4. DEVA SARMA, P. K.; ROY, R.: A Data Warehouse for Mining Usage Pattern in Library Transaction Data. In Assam University Journal of Science & Technology : Physical Sciences and Technology, Vol. 6, No. 2, 2010, 125 – 129.
  5. DUMITRESCU, G.; FILIP, F.-G.; IONiţĂ, A.; LEPĂDATU, C.: Open Source Eminescu’s Manuscripts: A Digitization Experiment. In Studies in Informatics and Control, vol. 19 , no. 1, 2010, pp 79 – 84.
  6. DURIEUX, V.; GEVENOIS, P. A.: Bibliometric indicators: quality measurements of scientific publication. In RADIOLOGY, 255 (2), 2010, pp. 342 – 351.
  7. FILIP, F.-G.: Sisteme suport pentru decizii. Ed. a 2-a, Bucureşti: Editura Tehnică, 2007, 364p.
  8. GOLFARELLI, M.; RIZZI, S.: Data Warehouse Design: Modern Principles and Methodologies. McGraw-Hill, 2009, 445 p.
  9. HOMAYOUNVALA, E.; JALALIMANESH, A.: Promoting research collaboration based on data mining techniques in library information systems. In International Journal of Information Technology and Business Management, Vol.8, No. 1, 2012, pp. 73 – 82.
  1. INS – Institutul Naţional de Statistică (2012) Cult1 – Activitatea  În Chestionare statistice, Statistica culturii. (http://www.insse.ro/cms/files/chestionare/cult/CULT1%202012.pdf )
  2. IONiţĂ, A.; LEPĂDATU, C.; DUMITRESCU, G.: Digital Cultural Landscape Content. În: HERNIK, Jozef (edit.) Cultural Landscape – Across Disciplines, Oficyna Wydawnicza BRANTA, Kracow, 2009, 255 – 277.
  3. ISO (2009). TR28118 Information and documentation – Performance indicators for national libraries. (http://www.iso.org/iso/home/store/catalogue_ics/ )
  4. KRIEGEL, H.-P.; KRÖGER, P.; SANDER, J.; ZIMEK, A.: Density-based clustering. In WIREs Data Mining and Knowledge Discovery, 1(3), 2011, pp. 231–240.
  5. LEPĂDATU, C.: Acquisition Policy of a Library and Data Mining Techniques. În: Studies in Informatics and Control, vol. 16, nr. 4, 2007, pp. 413 – 420.
  6. LEPĂDATU, C.: Sistem suport pentru decizii în cultura cunoaşterii. In: Revista română de biblioteconomie şi ştiinţele informării, Anul 4, nr. 2, 2008, pp. 41 – 45.
  7. LEPĂDATU, C.: Soluţii informatice pentru descoperirea cunoştinţelor din date / mineritul datelor. Referat doctoral nr. 1, Institutul de Cercetări pentru Inteligenţă Artificială „Mihai Drăgănescu” al Academiei Române, 2011 (http://www.racai.ro/media/Referatnr1SI-MDDCCornelLepadatu_sec.pdf )
  8. LEPĂDATU, C.: Explorarea datelor şi descoperirea cunoştinţelor – probleme, obiective şi strategii. In Revista Română de Informatică şi Automatică, vol.22, nr. 4, 2012, pp. 5 – 14.
  9. LEPĂDATU, C.: Metode exploratorii multidimensionale. In Revista Română de Informatică şi Automatică, vol. 23, nr. 1, 2013, pp. 14 – 30.
  10. LEPĂDATU, C.: Sistem pentru asistarea deciziilor bazat pe descoperirea cunoştinţelor din date. Referate doctorale nr. 2/3, Institutul de Cercetări pentru Inteligenţă Artificială „Mihai Drăgănescu” al Academiei Române, aprilie 2012 / februarie 2014. (http://www.racai.ro/media/Referat2-CornelLepadatu.pdf) (http://www.racai.ro/media/CLepadatu-Referat3.pdf ).
  11. LEPĂDATU, C.: Sisteme suport pentru decizii şi bibliomining. În Revista Română de Informatică şi Automatică, vol. 24, nr. 2, 2014, pp. 17–30.
  12. MISHRA, R.-N.; MISHRA, A.: Relevance of Data Mining in Digital Library. În International Journal of Future Computer and Communication, Vol. 2, No. 1, 2013, pp. 10 – 14.
  13. MOORE, M.; LOPER, K. A.: An Introduction to Clinical Decision Support Systems. În Electron Resour Med Libr., University of Miami, 2011, 31p.
  14. NICHOLSON, S.: The Basis for Bibliomining: Frameworks for Bringing Together Usage-Based Data Mining and Bibliometrics through Data Warehousing in Digital Library Services. In Information Processing and Management 42(3), 2006, pp. 785–804.
  15. PENDLEBURY, D.A.: Using Bibliometrics in Evaluating Research,
  16. (http://wokinfo.com/media/mtrp/UsingBibliometricsinEval_WP.pdf )
  17. SREENIVASARAO, V.; PALLAMREDDY, V.-S.: Advanced Data Warehousing Techniques for Analysis, Interpretation and Decision Support of Scientific Data. Springer-Verlag Berlin Heidelberg, D.C. Wyld et al. (Eds.): ACITY 2011, CCIS 198, 2011, pp.162–174.
  18. TRIA (DI), F.; LEFONS, E.; TANGORRA, F.: Hybrid methodology for data warehouse conceptual design by UML schemas. In Information and Software Technology, 54, 2012, pp. 360–379.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

| Designed by ICI București
© Copyright 2008-2023, All Rights Reserved