IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i12p1245-d85307.html
   My bibliography  Save this article

Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management

Author

Listed:
  • Yan Li

    (Center for Information Systems and Technology (CISAT), Claremont Graduate University, 130 E. Ninth St. ACB225, Claremont, CA 91711, USA)

  • Manoj Thomas

    (Information Systems, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • Kweku-Muata Osei-Bryson

    (Information Systems, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • Jason Levy

    (Public Administration, University of Hawaii, West Oahu, Kapolei, HI 97607, USA)

Abstract

With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environmental risk management problem is essential. In this emerging field, there is limited research dealing with the use of decision support to elicit environmental risk management (ERM) objectives and identify analytical goals from ERM decision makers. In this paper, we address problem formulation in the ERM understanding phase of the KDDA process. We build a DM 3 ontology to capture ERM objectives and to inference analytical goals and associated analytical techniques. A framework to assist decision making in the problem formulation process is developed. It is shown how the ontology-based knowledge system can provide structured guidance to retrieve relevant knowledge during problem formulation. The importance of not only operationalizing the KDDA approach in a real-world environment but also evaluating the effectiveness of the proposed procedure is emphasized. We demonstrate how ontology inferencing may be used to discover analytical goals and techniques by conceptualizing Hazardous Air Pollutants (HAPs) exposure shifts based on a multilevel analysis of the level of urbanization (and related economic activity) and the degree of Socio-Economic Deprivation (SED) at the local neighborhood level. The HAPs case highlights not only the role of complexity in problem formulation but also the need for integrating data from multiple sources and the importance of employing appropriate KDDA modeling techniques. Challenges and opportunities for KDDA are summarized with an emphasis on environmental risk management and HAPs.

Suggested Citation

  • Yan Li & Manoj Thomas & Kweku-Muata Osei-Bryson & Jason Levy, 2016. "Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management," IJERPH, MDPI, vol. 13(12), pages 1-17, December.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:12:p:1245-:d:85307
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/12/1245/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/13/12/1245/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ikujiro Nonaka, 1994. "A Dynamic Theory of Organizational Knowledge Creation," Organization Science, INFORMS, vol. 5(1), pages 14-37, February.
    2. Qiang Yang & Xindong Wu, 2006. "10 Challenging Problems In Data Mining Research," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 597-604.
    3. Keeney, Ralph L., 1996. "Value-focused thinking: Identifying decision opportunities and creating alternatives," European Journal of Operational Research, Elsevier, vol. 92(3), pages 537-549, August.
    4. Kenneth J. Arrow, 1950. "A Difficulty in the Concept of Social Welfare," Journal of Political Economy, University of Chicago Press, vol. 58(4), pages 328-328.
    5. Ramanathan, R. & Ganesh, L. S., 1994. "Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members' weightages," European Journal of Operational Research, Elsevier, vol. 79(2), pages 249-265, December.
    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. Zahir, Sajjad, 1999. "Geometry of decision making and the vector space formulation of the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 112(2), pages 373-396, January.
    2. Malcolm J. Beynon, 2006. "The Role of the DS/AHP in Identifying Inter-Group Alliances and Majority Rule Within Group Decision Making," Group Decision and Negotiation, Springer, vol. 15(1), pages 21-42, January.
    3. Torbjørn Bjerga & Terje Aven, 2016. "Some perspectives on risk management: A security case study from the oil and gas industry," Journal of Risk and Reliability, , vol. 230(5), pages 512-520, October.
    4. Csaba, László, 2014. "Átmenettan és közgazdaságtan. Módszertani tanulságok egy részterület műveléséből [Transitology" and economics. Methodological lessons to be drawn from work in a partial territory]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 53-67.
    5. Lu, Jinfeng & Dimov, Dimo, 2023. "A system dynamics modelling of entrepreneurship and growth within firms," Journal of Business Venturing, Elsevier, vol. 38(3).
    6. Olunifesi Adekunle Suraj, 2016. "Managing Telecommunications for Development: An Analysis of Intellectual Capital in Nigerian Telecommunication Industry," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-30, March.
    7. Soufiane Mezzourh & Walid A Nakara, 2009. "Governance and innovation : A Knowledge-based approach [La gouvernance de l'innovation : une approche par la connaissance]," Post-Print halshs-01955966, HAL.
    8. M. Max Evans & Ilja Frissen & Anthony K. P. Wensley, 2018. "Organisational Information and Knowledge Sharing: Uncovering Mediating Effects of Perceived Trustworthiness Using the PROCESS Approach," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-29, March.
    9. Crispin H. V. Cooper, 2020. "Quantitative Models of Well-Being to Inform Policy: Problems and Opportunities," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
    10. Xu, Zeshui, 2005. "Deviation measures of linguistic preference relations in group decision making," Omega, Elsevier, vol. 33(3), pages 249-254, June.
    11. Chris Kimble & José Braga Vasconcelos & Álvaro Rocha, 2016. "Competence management in knowledge intensive organizations using consensual knowledge and ontologies," Information Systems Frontiers, Springer, vol. 18(6), pages 1119-1130, December.
    12. Maurizio Zollo, 1998. "Strategies or Routines ? Knowledge Codification, Path-Dependence and the Evolution of Post-Acquisition Integration Practices in the U.S. Banking Industry," Center for Financial Institutions Working Papers 97-10, Wharton School Center for Financial Institutions, University of Pennsylvania.
    13. Duniesky Feitó Madrigal & Alejandro Mungaray Lagarda & Michelle Texis Flores, 2016. "Factors associated with learning management in Mexican micro-entrepreneurs," Estudios Gerenciales, Universidad Icesi, vol. 32(141), pages 381-386, December.
    14. Yildiz, H. Emre & Murtic, Adis & Zander, Udo, 2024. "Re-conceptualizing absorptive capacity: The importance of teams as a meso-level context," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    15. David Vallat, 2015. "Une alternative au dualisme État-Marché : l’économie collaborative, questions pratiques et épistémologiques," Working Papers halshs-01249308, HAL.
    16. Gaviria-Marin, Magaly & Merigó, José M. & Baier-Fuentes, Hugo, 2019. "Knowledge management: A global examination based on bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 194-220.
    17. Christoph P. Kiefer & Pablo Del Río González & Javier Carrillo‐Hermosilla, 2019. "Drivers and barriers of eco‐innovation types for sustainable transitions: A quantitative perspective," Business Strategy and the Environment, Wiley Blackwell, vol. 28(1), pages 155-172, January.
    18. Kotaro Suzumura, 2020. "Reflections on Arrow’s research program of social choice theory," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 54(2), pages 219-235, March.
    19. Leo Katz & Alvaro Sandroni, 2020. "Limits on power and rationality," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 54(2), pages 507-521, March.
    20. Ni Li & Minghui Sun & Zhuming Bi & Zeya Su & Chao Wang, 2014. "A new methodology to support group decision-making for IoT-based emergency response systems," Information Systems Frontiers, Springer, vol. 16(5), pages 953-977, November.

    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:gam:jijerp:v:13:y:2016:i:12:p:1245-:d:85307. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.