Knowledge Discovery System and Their Challenges in Property Company: A Systematic Literature Review

Authors

  • Erisa Rizkyani Universitas Indonesia
  • Dea Valentina Universitas Indonesia
  • Jonathan Sofian Lusa Universitas Indonesia
  • Nadya Safitri Universitas Indonesia

DOI:

https://doi.org/10.32493/jtsi.v9i1.55838

Keywords:

Data-Driven Decision-Making, Knowledge Discovery Systems, Machine Learning, PRISMA, Property Companies, Systematic Literature Review

Abstract

In the era of digital transformation and data-driven decision-making, property companies are increasingly challenged by the complexity of managing vast, diverse, and unstructured data. Knowledge Discovery Systems (KDS) have emerged as vital tools for extracting valuable insights to support strategic functions such as property valuation, market analysis, and urban planning. This paper aims to investigate the trends and challenges in implementing KDS in property companies through a Systematic Literature Review (SLR) using the PRISMA framework. A total of 23 relevant publications from 2020 to 2025 were reviewed. The study finds that KDS applications span from real estate price prediction using machine learning to knowledge representation using semantic models. However, the implementation of KDS still faces significant barriers such as limited interdisciplinary collaboration, poor data quality, domain-specific constraints, and resistance to technological adoption. The results of this review contribute to a better understanding of how KDS can be effectively utilized in the property sector. It also highlights the need for future research to improve system adaptability, model explainability, and integration with domain knowledge to foster more intelligent, data-driven organizations

References

Abdulaziz, M. H., & Zeki, A. M. (2020). Prediction of Real Estate Land Prices in the Kingdom of Bahrain. 2020 International Conference on Decision Aid Sciences and Application, DASA 2020, 1220–1223. https://doi.org/10.1109/DASA51403.2020.9317063

Abdul-Rahman, S., Mutalib, S., Alam, S., Nor, M., Zulkifley, H., & Ibrahim, M. I. (2021). Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur. IJACSA) International Journal of Advanced Computer Science and Applications, 12(12), 736–745. https://doi.org/10.14569/IJACSA.2021.0121291

Acevedo, J., & Diaz-Molina, I. (2023). Learning organizations in emerging economies: the effect of knowledge management on innovative culture in Chilean companies. Learning Organization, 30(1), 37–54. https://doi.org/10.1108/TLO-01-2021-0009

Ahn, I. S., Kim, J. J., & Lee, J. S. (2024). Development of a Cost Prediction Model for Design Changes: Case of Korean Apartment Housing Projects. Sustainability (Switzerland) , 16(11), 4322. https://doi.org/10.3390/su16114322

Alby, T. (2024). Bridging the Analytics Gap: Optimizing Content Performance using Actionable Knowledge Discovery. HT 2024: Creative Intelligence - 35th ACM Conference on Hypertext and Social Media, 185–192. https://doi.org/10.1145/3648188.3675121

Aparicio, J. T., Arsenio, E., Santos, F., & Henriques, R. (2024). Using dynamic knowledge graphs to detect emerging communities of knowledge. Knowledge-Based Systems, 294, 111671. https://doi.org/10.1016/j.knosys.2024.111671

Ayyasamy, R. K., Tahavna, B., Subramaniam, S., Tan, F. J., Krisnan, S., & Tahayna, L. N. A. (2022). Design and Implementation of Residential Rental Rates Forecast Model using Data Mining Algorithms. 2022 3rd International Conference on Artificial Intelligence and Data Sciences: Championing Innovations in Artificial Intelligence and Data Sciences for Sustainable Future, AiDAS 2022 - Proceedings, 176–181. https://doi.org/10.1109/AiDAS56890.2022.9918792

Azizah, W., Irma Purnamasari, A., & Bahtiar, A. (2025). Journal of Artificial Intelligence and Engineering Applications Clustering Analysis of Administrative Service Types Using K-Means (Study Case: Village bojongsalam). 4(2), 1303–1310. https://doi.org/https://doi.org/10.59934/jaiea.v4i2.867

Becerra-Fernandez, I., & Sabherwal, R. (2015). Knowledge Management (Second Edition).

Carrera-Rivera, A., Larrinaga, F., & Lasa, G. (2022). Context-awareness for the design of Smart-product service systems: Literature review. Computers in Industry, 9, 101895. https://doi.org/10.1016/j.compind.2022.103730

Chuang, H. C., Chen, C. C., & Li, S. T. (2024). Advancing SVM classification: Parallelizing conjugate gradient for monotonicity enforcement. Knowledge-Based Systems, 302, 112388. https://doi.org/10.1016/j.knosys.2024.112388

Das, S. S. S., Ali, M. E., Li, Y. F., Kang, Y. Bin, & Sellis, T. (2021). Boosting house price predictions using geo-spatial network embedding. Data Mining and Knowledge Discovery, 35(6), 2221–2250. https://doi.org/10.1007/s10618-021-00789-x

Dong, N. (2024). Research on Knowledge Discovery Service in Digital Libraries Based on Deep Learning. 2024 13th International Conference on Educational and Information Technology, ICEIT 2024, 328–333. https://doi.org/10.1109/ICEIT61397.2024.10540902

Duan, G., & Dong, J. (2024). Construction of Ensemble Learning Model for Home Appliance Demand Forecasting. Applied Sciences (Switzerland), 14(17), 7658. https://doi.org/10.3390/app14177658

Gloudemans, R., & Sanderson, P. (2021). The Potential of Artificial Intelligence in Property Assessment. Journal of Property Tax Assessment & Administration •, 18(2), 87–106. https://doi.org/https://doi.org/10.63642/1357-1419.1241

Horváthová, J., Mokrišová, M., & Schneider, A. (2024). The Application of Machine Learning in Diagnosing the Financial Health and Performance of Companies in the Construction Industry. Information (Switzerland), 15(6), 355. https://doi.org/10.3390/info15060355

Huang, C., Yang, Y., Wang, H., Zhang, X., Zhao, J., & Wan, J. (2021). Research and application of data mining algorithm. Journal of Physics: Conference Series, 1748(3), 32043. https://doi.org/10.1088/1742-6596/1748/3/032043

Huang, Y., Shi, Q., Zuo, J., Pena-Mora, F., & Chen, J. (2021). Research Status and Challenges of Data-Driven Construction Project Management in the Big Data Context. Advances in Civil Engineering, 2021(1), 6674980. https://doi.org/10.1155/2021/6674980

Jiang, Y. (2022). Application of Data Mining Technology in Enterprise Human Resource Management Informatization. Proceedings - 2022 International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022, 228–232. https://doi.org/10.1109/AIARS57204.2022.00058

Kang, J., Lee, H. J., Jeong, S. H., Lee, H. S., & Oh, K. J. (2020). Developing a forecasting model for real estate auction prices using artificial intelligence. Sustainability (Switzerland), 12(7), 2899. https://doi.org/10.3390/su12072899

Kou, J., & Gedik, Y. (2019). New Behavioural Big Data Methods for Predicting Housing Price. EAI Endorsed Transactions on Scalable Information Systems, 6(21), 1–8. https://doi.org/10.4108/eai.13-7-2018.158418

Kumara, W. A., Astuti, R., Prihartono, W., & Suprapti, T. (2025). Journal of Artificial Intelligence and Engineering Applications Analysis of Beverage Sales Data Using the FP-Growth Algorithm at Sini Aja Cafe. 4(2), 2808–4519. https://doi.org/https://doi.org/10.59934/jaiea.v4i2.772

Laovisutthichai, V., & Lu, W. (2023). Design for manufacture and assembly (DfMA) in architectural design meetings: from a case study to knowledge-to-action framework. Smart and Sustainable Built Environment, 12(5), 1117–1134. https://doi.org/10.1108/SASBE-07-2022-0136

Lazoglou, M., & Angelides, D. C. (2020). Development of a spatial decision support system for land-use suitability assessment: The case of complex tourism accommodation in Greece. Research in Globalization, 2, 100022. https://doi.org/10.1016/j.resglo.2020.100022

Li, C., Qian, P., Xu, N., Jiang, C., Wang, Y., Wang, Y., & Guoming, M. A. (2021). Operating Status Diagnosis of Power Equipment Based on Rule Engine. 2021 Electrical Insulation Conference, EIC 2021, 673–677. https://doi.org/10.1109/EIC49891.2021.9612402

Mathotaarachchi, K. V., Hasan, R., & Mahmood, S. (2024). Advanced Machine Learning Techniques for Predictive Modeling of Property Prices. Information (Switzerland), 15(6), 295. https://doi.org/10.3390/info15060295

Mohammadrezaei, E., Ghasemi, S., Dongre, P., Gracanin, D., & Zhang, H. (2024). Systematic Review of Extended Reality for Smart Built Environments Lighting Design Simulations. IEEE Access, 12, 17058–17089. https://doi.org/10.1109/ACCESS.2024.3359167

Molina-Coronado, B., Mori, U., Mendiburu, A., & Miguel-Alonso, J. (2020). Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process. IEEE Transactions on Network and Service Management, 17(4), 2451–2479. https://doi.org/10.1109/TNSM.2020.3016246

Moretto, V., Elia, G., Schirinzi, S., Vizzi, R., & Ghiani, G. (2022). A knowledge visualization approach to identify and discovery inner areas: a pilot application in the province of Lecce. Management Decision, 60(4), 1132–1158. https://doi.org/10.1108/MD-01-2021-0104

Mydyti, H., Kadriu, A., & Pejic Bach, M. (2023). Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development. Organizacija, 56(2), 138–154. https://doi.org/10.2478/orga-2023-0010

Ni, G., Zhou, Q., Miao, X., Niu, M., Zheng, Y., Zhu, Y., & Ni, G. (2025). What and how influence the safety knowledge sharing of new generation of construction workers in China: a study based on DEMATEL and ISM. Engineering, Construction and Architectural Management, 32(4), 2160–2189. https://doi.org/10.1108/ECAM-11-2022-1065

Özöğür Akyüz, S., Eygi Erdogan, B., Yıldız, Ö., & Karadayı Ataş, P. (2023). A Novel Hybrid House Price Prediction Model. Computational Economics, 62(3), 1215–1232. https://doi.org/10.1007/s10614-022-10298-8

Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., Mcdonald, S., … Mckenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. In The BMJ (Vol. 372, p. n160). BMJ Publishing Group. https://doi.org/10.1136/bmj.n160

Rabhi, F. A., Bandara, M., Lu, K., & Dewan, S. (2021). Design of an innovative IT platform for analytics knowledge management. Future Generation Computer Systems, 116, 209–219. https://doi.org/10.1016/j.future.2020.10.022

Rohayati, E., Rinaldi Dikananda, A., & Rohman, D. (2025). Journal of Artificial Intelligence and Engineering Applications Sales Data Analysis Using Linear Regression Algorithm on Raw Water Sales. Journal of Artificial Intelligence and Engineering Applications, 4(2), 2808–4519. https://doi.org/ttps://doi.org/10.59934/jaiea.v4i2.809

Shu, X., & Ye, Y. (2023). Knowledge Discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. https://doi.org/10.1016/j.ssresearch.2022.102817

Soltani, A., Pettit, C. J., Heydari, M., & Aghaei, F. (2021). Housing price variations using spatio-temporal data mining techniques. Journal of Housing and the Built Environment, 36(3), 1199–1227. https://doi.org/10.1007/s10901-020-09811-y

Tian, D., Li, M., Shen, Y., & Han, S. (2023). Intelligent mining of safety hazard information from construction documents using semantic similarity and information entropy. Engineering Applications of Artificial Intelligence, 119. https://doi.org/10.1016/j.engappai.2022.105742

Tokede, O., Ahiaga-Dagbui, D., & Morrison, J. (2022). Praxis of knowledge-management and trust-based collaborative relationships in project delivery: mediating role of a project facilitator. International Journal of Managing Projects in Business, 15(4), 595–618. https://doi.org/10.1108/IJMPB-03-2021-0072

Tripathi, S., Muhr, D., Brunner, M., Jodlbauer, H., Dehmer, M., & Emmert-Streib, F. (2021). Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing. Frontiers in Artificial Intelligence, 4, 576892. https://doi.org/10.3389/frai.2021.576892

Wei, C., Fu, M., Wang, L., Yang, H., Tang, F., & Xiong, Y. (2022). The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data. Land, 11(3), 334. https://doi.org/10.3390/land11030334

Zhou, Q., Deng, X., Wang, G., Mahmoudi, A., & Zhang, N. (2024). Exploring the key drivers of inter-organizational knowledge transfer in projects: evidence from international construction projects. Engineering, Construction and Architectural Management, 31(3). https://doi.org/10.1108/ECAM-05-2024-0692

Downloads

Published

2026-01-31

How to Cite

Erisa Rizkyani, Dea Valentina, Jonathan Sofian Lusa, & Nadya Safitri. (2026). Knowledge Discovery System and Their Challenges in Property Company: A Systematic Literature Review. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 9(1), 1–12. https://doi.org/10.32493/jtsi.v9i1.55838

Issue

Vol. 9 No. 1 (2026): Jurnal Teknologi Sistem Informasi dan Aplikasi

Section

Article

Categories

License

Copyright (c) 2026 Erisa Rizkyani, Dea Valentina, Jonathan Sofian Lusa, Nadya Safitri

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.

In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License

YOU ARE FREE TO:

  • Adapt - remix, transform, and build upon the material for any purpose, even commercially.
  • The licensor cannot revoke these freedoms as long as you follow the license terms