IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/51547.html
   My bibliography  Save this paper

Decision Support Systems Usefulness and A Practical Solution Based on Semantic Web Technologies

Author

Listed:
  • necula, sabina-cristiana
  • Radu, Laura-Diana

Abstract

Nowadays Decision Support Systems deal with impressive amount of information. Current Decision Support Systems are customized solutions, possible to be used only in the context for which they were developed. In addition to this the information integration is other common problem of the Decision Support Systems. This paper tries to outline the main idea that in order that Decision Support Systems users' to be satisfied with the solution provided two conditions must be assured: the possibility to apply knowledge at the decision moment and place by the decision makers and the semantic integration of information. In this work we analyze the direct influence of the two conditions enunciated above and we came with a solution that is based on ontology, Semantic Web technologies and inference engine. We demonstrate the contribution of our approach by undertaking three scenarios from business decision making processes.

Suggested Citation

  • necula, sabina-cristiana & Radu, Laura-Diana, 2011. "Decision Support Systems Usefulness and A Practical Solution Based on Semantic Web Technologies," MPRA Paper 51547, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:51547
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/51547/1/MPRA_paper_51547.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    2. Frada Burstein & Clyde Holsapple, 2008. "Handbook on Decision Support Systems 1," International Handbooks on Information Systems, Springer, number 978-3-540-48713-5, September.
    3. Brandon A. Beemer & Dawn G. Gregg, 2008. "Advisory Systems to Support Decision Making," International Handbooks on Information Systems, in: Handbook on Decision Support Systems 1, chapter 24, pages 511-527, Springer.
    4. Maryam Alavi & Amrit Tiwana, 2002. "Knowledge integration in virtual teams: The potential role of KMS," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(12), pages 1029-1037, October.
    5. Bielza, Concha & Gómez, Manuel & Shenoy, Prakash P., 2011. "A review of representation issues and modeling challenges with influence diagrams," Omega, Elsevier, vol. 39(3), pages 227-241, June.
    Full references (including those not matched with items on IDEAS)

    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. Hamed M. Zolbanin & Dursun Delen & Durand Crosby & David Wright, 2020. "A Predictive Analytics-Based Decision Support System for Drug Courts," Information Systems Frontiers, Springer, vol. 22(6), pages 1323-1342, December.
    2. Hamed M. Zolbanin & Dursun Delen & Durand Crosby & David Wright, 0. "A Predictive Analytics-Based Decision Support System for Drug Courts," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    3. Tyrychtr, J., 2017. "Analytical System with Decision Tree for Economic Benefit," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 9(4).
    4. Mark Gilchrist & Deana Lehmann Mooers & Glenn Skrubbeltrang & Francine Vachon, 2012. "Knowledge Discovery in Databases for Competitive Advantage," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 3(2), pages 2-15, April.
    5. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    6. Zhang, Zhiwang & Gao, Guangxia & Shi, Yong, 2014. "Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors," European Journal of Operational Research, Elsevier, vol. 237(1), pages 335-348.
    7. Manuel Casal-Guisande & Alberto Comesaña-Campos & Alejandro Pereira & José-Benito Bouza-Rodríguez & Jorge Cerqueiro-Pequeño, 2022. "A Decision-Making Methodology Based on Expert Systems Applied to Machining Tools Condition Monitoring," Mathematics, MDPI, vol. 10(3), pages 1-30, February.
    8. Maysam Eftekhary & Peyman Gholami & Saeed Safari & Mohammad Shojaee, 2012. "Ranking Normalization Methods for Improving the Accuracy of SVM Algorithm by DEA Method," Modern Applied Science, Canadian Center of Science and Education, vol. 6(10), pages 1-26, October.
    9. Marta Kadłubek & Eleftherios Thalassinos & Joanna Domagała & Sandra Grabowska & Sebastian Saniuk, 2022. "Intelligent Transportation System Applications and Logistics Resources for Logistics Customer Service in Road Freight Transport Enterprises," Energies, MDPI, vol. 15(13), pages 1-27, June.
    10. Duan, Yunlong & Wang, Wannan & Zhou, Wei, 2020. "The multiple mediation effect of absorptive capacity on the organizational slack and innovation performance of high-tech manufacturing firms: Evidence from Chinese firms," International Journal of Production Economics, Elsevier, vol. 229(C).
    11. Ramli, Azizul Azhar & Watada, Junzo & Pedrycz, Witold, 2011. "Real-time fuzzy regression analysis: A convex hull approach," European Journal of Operational Research, Elsevier, vol. 210(3), pages 606-617, May.
    12. Carrizosa, Emilio & Guerrero, Vanesa & Romero Morales, Dolores, 2018. "On Mathematical Optimization for the visualization of frequencies and adjacencies as rectangular maps," European Journal of Operational Research, Elsevier, vol. 265(1), pages 290-302.
    13. Lijie Feng & Zhenzhen Zhao & Jinfeng Wang & Ke Zhang, 2022. "The Impact of Knowledge Management Capabilities on Innovation Performance from Dynamic Capabilities Perspective: Moderating the Role of Environmental Dynamism," Sustainability, MDPI, vol. 14(8), pages 1-19, April.
    14. Khakzad, Nima, 2021. "Optimal firefighting to prevent domino effects: Methodologies based on dynamic influence diagram and mathematical programming," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    15. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.
    16. Blanquero, Rafael & Carrizosa, Emilio & Molero-Río, Cristina & Romero Morales, Dolores, 2020. "Sparsity in optimal randomized classification trees," European Journal of Operational Research, Elsevier, vol. 284(1), pages 255-272.
    17. Rufo, M.J. & Martín, J. & Pérez, C.J., 2016. "A Bayesian negotiation model for quality and price in a multi-consumer context," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 132-141.
    18. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    19. Oerlemans, Leon & Chan, K.Y. & Knoben, Joris & Vermeulen, P.A.M., 2018. "Structural and geographical conditions for exploitative innovation : Evidence from South African manufacturing firms," Other publications TiSEM 4abcf615-a0d4-49ef-ba25-c, Tilburg University, School of Economics and Management.
    20. Carolina Lino Martins & Pascale Zaraté & Adiel Teixeira de Almeida & Jônatas Araújo de Almeida & Danielle Costa Morais, 2021. "Web-Based DSS for Resource Allocation in Higher Education," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 13(4), pages 1-23, October.

    More about this item

    Keywords

    decision support systems; information integration; semantic web; ontology; knowledge;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

    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:pra:mprapa:51547. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.