Design of a Big-data-Based Decision Support System for Rational Cultural Policy Establishment

Youngseok Lee - Department of Computer Education, Seoul National University of Education, 96 Seochojungang-ro, Seocho-gu, Seoul, Republic of Korea
Gimoon Cho - Department of Industrial Data Science, Kangnam University, 40 Gangnam-ro Giheung-gu, Yongin-si, Gyeonggi-do, Republic of Korea
Jungwon Cho - Department of Computer Education, Jeju National University, 102 Jejudaehakno, Jeju-si, Jeju-do, Republic of Korea

Citation Format:



This paper proposes a technique for designing a decision-making system based on big data to support rational cultural policy decisions. To identify a rational cultural policy, it is necessary to extract a comparable index for cultural policy and analyze and process factors in terms of cultural supply and cultural consumption. Analyzed and processed supply indices and consumption indices become the basic input data for calculating additional cultural indices that can be measured at the cultural level of each region. Regional cultural indices are treated as independent variables in terms of cultural supply, and target variables are considered in terms of cultural demand. Two corresponding types of regression models are established. Based on the eXtreme gradient boosting and light gradient boosting machine algorithms, which are representative algorithms for calculating cultural indicators, we attempted to construct and analyze a model of the proposed system. The developed model is designed to predict the demand index according to the regional cultural supply index. It was confirmed that the demand side could be changed based on supply-side items by using the proposed technique to support decision-making. Due to the complexity of the policy environment of modern society, mixing various policy tools targeting multiple functions is accepted as a common basis for policy design, but institutional arrangements are needed to reflect the results of various data analyses in budget decision-making. This will be possible to produce data based on effectiveness and suggest appropriate rational policies and decisions.


Culture supply policy; cultural figures; balanced culture; big data; decision-making system.

Full Text:



H.-Y. Choi, E.-H. Seo, and H.-J. Jun, “Social Diversity and Quality of Life among People Living in Seoul: An Application of Multilevel Model,†Journal of the Korean Regional Science Association, vol. 36, no. 1, pp. 69–88, Mar. 2020. DOI : 10.22669/krsa.2020.36.1.069.

Ahn, P., “Role of Senior Program for Financial Independence in Culture and Art Facility,†Journal of Korean Society for Quality Management, vol. 48, no. 2, pp. 345-359. 2020. DOI : 10.7469/JKSQM.2020.48.2.345.

Jeon, C. H., Kim, D. J., Kim, S. K., Kim, D. J., Lee, H. M., & Park, H. J., “Validation in the cross-cultural adaptation of the Korean version of the Oswestry Disability Index,†Journal of Korean medical science, vol. 21, no. 6, pp. 1092-1097, 2006. DOI: 10.3346/jkms.2006.21.6.1092.

Kim, B. R., Shin, J., Guevarra, R., Lee, J. H., Kim, D. W., Seol, K. H., & Isaacson, R. E., “Deciphering diversity indices for a better understanding of microbial communities,†J Microbiol Biotechnol, vol. 27, no. 12, pp. 2089-2093, 2017. DOI: 10.4014/jmb.1709.09027.

Kim, H. Y., & Cho, J. S., “Data governance framework for big data implementation with a case of Korea,†In 2017 IEEE International Congress on Big Data (BigData Congress), pp. 384-391, 2017. DOI: 10.1109/BigDataCongress.2017.56.

Engin, Z., & Treleaven, P., “Algorithmic government: Automating public services and supporting civil servants in using data science technologies,†The Computer Journal, vol. 62, no. 3, pp. 448-460, 2019. DOI: 10.1093/comjnl/bxy082.

Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X., “Big data challenge: a data management perspective,†Frontiers of computer Science, vol. 7, no. 2, pp. 157-164, 2013.

Chun, S., Shulman, S., Sandoval, R., & Hovy, E., “Government 2.0: Making connections between citizens, data and government,†Information Polity, vol. 15, no. 1-2, pp. 1-9, 2010. DOI 10.3233/IP-2010-0205.

Vanderlinde, R., Dexter, S., & van Braak, J., “Schoolâ€based ICT policy plans in primary education: Elements, typologies and underlying processes,†British Journal of Educational Technology, vol. 43, no. 3, pp. 505-519, 2012.

Visvizi, Anna, et al., “Policy making for smart cities: Innovation and social inclusive economic growth for sustainability,†Journal of Science and Technology Policy Management. 2018.

Park, E., “Positive or negative? Public perceptions of nuclear energy in South Korea: Evidence from Big Data,†Nuclear Engineering and Technology, vol. 51, no. 2, pp. 626-630, 2019.

Kim, E. S., Choi, Y., & Byun, J., “Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs,†Sustainability, vol. 12, no. 1, p. 202, 2020.

Olszewski, R., Pałka, P., Wendland, A., & Majdzińska, K., “Application of cooperative game theory in a spatial context: An example of the application of the community-led local development instrument for the decision support system of biogas plants construction,†Land Use Policy, 108, 105485, 2021.

Ordóñez de Pablos, P., & Lytras, M., “Knowledge management, innovation and big data: Implications for sustainability, policy making and competitiveness,†Sustainability, vol. 10, no. 6, 2018. DOI : 10.3390/su10062073.

Yoon, D., “The information science policy for the public open data of the national research institute,†Cogent Business & Management, vol. 4, no. 1, 1406321, 2017. DOI : 10.1080/23311975.2017.1406321.

S. Kim, “A Study on Development of Policy Attributes Taxonomy for Data-based Decision Making,†The Journal of Information Systems, vol. 29, no. 3, pp. 1–34, Sep. 2020. DOI : 10.5859/KAIS.2020.29.3.1.

LG CNS, Difficulty identifying demand, can it be predicted with machine learning?,, (visited Nov. 2021).

R. B. Caldo and J. Palomaria, "Automated Driver’s Assessment and Driving Simulator System," International Journal of Advanced Science and Convergence, vol. 1, no. 1, pp. 23-31, 2019. DOI: 10.22662/IJASC.2019.1.1.023.

J. Lee and C. H. Lee, "A Study on Changes in Foreign Tourists Using Big Data Analysis Method," International Journal of Advanced Science and Convergence, vol. 2, no. 4, pp. 25-30, 2020. DOI: 10.22662/IJASC.2020.2.4.025.

Nishant, Rohit; Kennedy, Mike; Corbett, Jacqueline, “Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda,†International Journal of Information Management, 53: 102104, 2020.

Modgil, Sachin; Gupta, Shivam; Bhushan, Bharat, “Building a living economy through modern information decision support systems and UN sustainable development goals,†Production Planning & Control, 31.11-12, pp. 967-987, 2020.

Constantiou, Ioanna; Shollo, Arisa; Vendelø, Morten Thanning, “Mobilizing intuitive judgement during organizational decision making: When business intelligence is not the only thing that matters,†Decision Support Systems, 121: pp. 51-61, 2019.

Kim, Yeonsoo; Oh, Jooseok; Kim, Seiyong, “The Transition from Traditional Infrastructure to Living SOC and Its Effectiveness for Community Sustainability: The Case of South Korea,†Sustainability, 12.24: 10227, 2020.

Dahlgaard-Park, Su Mi; Reyes, Lidia; Chen, Chi-Kuang, “The evolution and convergence of total quality management and management theories,†Total Quality Management & Business Excellence, 29.9-10, pp. 1108-1128, 2018.

Abou Omar, Kamil Belkhayat, “XGBoost and LGBM for Porto Seguro’s Kaggle challenge: A comparison,†Preprint Semester Project, 2018.

Castelnovo, Paolo, Valentina Morretta, and Michela Vecchi, "Regional disparities and industrial structure: territorial capital and productivity in Italian firms," Regional Studies, vol. 54 no. 12, pp. 1709-1723, 2020.

Kim K., Hong S., “Analysis on Regional Disparities in Culture & Arts Infrastructure: Focused on Gyeonggi Province,†The Journal of Korean Policy Studies, vol. 21, no. 2, pp.27-50, 2021.

Culture Data Plaza, (visited Nov. 2021).

Public data portal,, (visited Nov. 2021).