research-article

Business Intelligence Framework Design and Implementation: A Real-estate Market Case Study

Authors:

Walid A. Salameh,

Ammar Elhassan,

Bushra Abu Tahoun,

Maisa AsasfehAuthors Info & Claims

ACM Journal of Data and Information Quality (JDIQ), Volume 13, Issue 2

Article No.: 10, Pages 1 - 16

https://doi.org/10.1145/3422669

Published: 30 June 2021 Publication History

Abstract

This article builds on previous work in the area of real-world applications of Business Intelligence (BI) technology. It illustrates the analysis, modeling, and framework design of a BI solution with high data quality to provide reliable analytics and decision support in the Jordanian real estate market. The motivation is to provide analytics dashboards to potential investors about specific segments or units in the market. The article ekxplains the design of a BI solution, including background market and technology investigation, problem domain requirements, solution architecture modeling, design and testing, and the usability of descriptive and predictive features. The resulting framework provides an effective BI solution with user-friendly market insights for investors with little or no market knowledge. The solution features predictive analytics based on established Machine Learning modeling techniques, analyzed and contrasted to select the optimum methodology and model combination for predicting market behavior to empower inexperienced users.

References

[1]

J. Kahr and M. C. Thomsett. 2006. Real Estate Market Valuation and Analysis. John Wiley & Sons, New York, NY, 2006.

[2]

A. N. Alfiyatin, R. E. Febrita, H. Taufiq, and W. F. Mahmudy. 2017. Modeling house price prediction using regression analysis and particle swarm optimization. Int. J. Adv. Comput. Sci. Appl. 8, 10 (2017).

[3]

H. Yu and J. Wu. 2016. Real Estate Price Prediction with Regression and Classification. M.Sc. Thesis. Stanford University, California, 2016.

[4]

D. Edge, J. Larson, and C. White. 2018. Bringing AI to BI: Enabling visual analytics of unstructured data in a modern business intelligence platform. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. 1–9.

Digital Library

[5]

A. N. H. Zaied, M. O. Grida, and G. S. Hussein. 2018. Evaluation of critical success factors for business intelligence systems using fuzzy ahp. J. Theor. Appl. Inf. Technol. 96, 19 (2018).

[6]

V. H. Trieu. 2017. Getting value from business intelligence systems: A review and research agenda. Decis. Support Syst. 93 (2017), 111–124. https://doi.org/10.1016/j.dss.2016.09.019

Digital Library

[7]

N. Jain, P. Goel, P. Sharma, and V. Deep. 2019. Prediction of house pricing using machine learning with python. In International Conference on Advances in Engineering Science Management & Technology (ICAESMT'19), Uttaranchal University, Dehradun, India. https://dx.doi.org/10.2139/ssrn.3403964

[8]

A. Ashrafi, A. Z. Ravasan, P. Trkman, and S. Afshari. 2019. The role of business analytics capabilities in bolstering firms’ agility and performance. Int. J. Inf. Manage. 47 (2019), 1–15.

Digital Library

[9]

G. Richards, W. Yeoh, A. Y. L. Chong, and A. Popovič. 2019. Business intelligence effectiveness and corporate performance management: an empirical analysis. J. Comput. Inf. Syst. 59, 2 (2019), 188–196.

[10]

B. Dumitru-Alexandru. 2016. Business intelligence for decision making in economics. In Artificial Intelligence in Financial Markets. Palgrave Macmillan, London, 125–158.

[11]

É. Scholly. 2019. Business intelligence & analytics applied to public housing. In Proceedings of the European Conference on Advances in Databases and Information Systems. Springer, Cham, 552–557.

[12]

M. Safadi, J. Ma, R. Wickramasuriya, D. Daly, P. Perez, and G. Kokogiannakis. 2017. Mapping for the future: Business intelligence tool to map regional housing stock. Proc. Eng. 180 (2017), 1684–1694.

[13]

M. Bahrami, S. M. Arabzad, and M. Ghorbani. 2012. Innovation in market management by utilizing business intelligence: Introducing proposed framework. Proc. Soc. Behav. Sci. 41 (2012), 160–167.

[14]

K. Kasemsap. 2016. The fundamentals of business intelligence. Int. J. Org. Collect. Intell. 6, 2 (2016), 12–25.

Digital Library

[15]

P. Wazurkar, R. S. Bhadoria, and D. Bajpai. 2017. Predictive analytics in data science for business intelligence solutions.

[16]

M. Arora and D. Chakrabarti. 2013. Application of business intelligence: A case on payroll management. In Proceedings of the International Symposium on Computational and Business Intelligence. IEEE.

Digital Library

[17]

C. El Morr and H. Ali-Hassan. 2019. Descriptive, predictive, and prescriptive analytics. In Analytics in Healthcare (31–55). Springer, Cham.

[18]

M. D. Shamout. 2019. Does supply chain analytics enhance supply chain innovation and robustness capability? Organizacija 52, 2 (2019), 95–106.

[19]

K. Lepenioti, A. Bousdekis, D. Apostolou, and G. Mentzas. 2020. Prescriptive analytics: Literature review and research challenges. Int. J. Inf. Manage. 50 (2020), 57–70.

Digital Library

[20]

D. Delen. 2019. Prescriptive Analytics: The Final Frontier for Evidence-based Management and Optimal Decision Making. FT Press.

[21]

J. Vater, L. Harscheidt, and A. Knoll. 2019. Smart manufacturing with prescriptive analytics. In Proceedings of the 2019 8th International Conference on Industrial Technology and Management (ICITM’19). IEEE, 224–228

[22]

N. Ain, G. Vaia, W. H. DeLone, and M. Waheed. 2019. Two decades of research on business intelligence system adoption, utilization and success—A systematic literature review. Decis. Support Syst. 125 (2019), 113113. https://doi.org/10.1016/j.dss.2019.113113

[23]

S. W. Tzang, C. H. Hung, C. P. Chang, and Y. S. Tsai. 2018. Commercial real estate evaluation: The real options approach. In Proceedings of the International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. Springer, Cham.

[24]

M. Yasen and N. Al-Madi. 2019. Improved swarm intelligence optimization using crossover and mutation for medical classification. In Proceedings of the 2nd International Conference on new Trends in Computing Sciences (ICTCS’19). IEEE, 1–6

[25]

R. Valverde. 2011. A business intelligence system for risk management in the real estate industry. Int. J. Comput. Appl. 27, 2 (2011), 14–22.

[26]

W. Wedyawati and M. Lu. 2004. Mining real estate listings using ORACLE data warehousing and predictive regression. In Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration.

[27]

M. Geiger and K. Stockinger. 2019. Data warehousing and exploratory analysis for market monitoring. In Applied Data Science. Springer, Cham, 333–351

[28]

A. S. Ravikumar. 2016. Real estate price prediction using machine learning. M.Sc. Thesis. School of Computing, National College of Ireland, Ireland.

[29]

H. Selim. 2009. Determinants of house prices in turkey: Hedonic regression versus artificial neural network. Expert Syst. Appl. 36, 2 (2009), 2843–2852.

Digital Library

[30]

S. Dasgupta and V. K. Vankayala. 2007. Developing real time business intelligence systems the agile way. In Proceedings of the 1st Annual IEEE Systems Conference. IEEE, 1–7.

[31]

D. Larson and V. Chang. 2016. A review and future direction of agile, business intelligence, analytics and data science. Int. J. Inf. Manage. 36, 5 (2016), 700–710.

Digital Library

[32]

N. H. Z. Abai, J. H. Yahaya, and A. Deraman. 2013. User requirement analysis in data warehouse design: A review. Proc. Technol. 11 (2013), 801–806.

[33]

Y. Wang, S. Yu, and T. Xu. 2017. A user requirement driven framework for collaborative design knowledge management. Adv. Eng. Inf. 33 (2017), 16–28.

Digital Library

[34]

A. Dennis, B. H. Wixom, and R. M. Roth. 2018. Systems Analysis and Design. John Wiley & Sons, New York, NY, 2018.

Digital Library

[35]

R. Sherman. 2014. Business Intelligence Guidebook. Morgan Kaufmann.

Digital Library

[36]

I. L. Ong, P. H. Siew, and S. F. Wong. 2014. A five-layered business intelligence architecture. Communications of the IBIMA (2014).

[37]

C. Saini and V. Arora. 2016. Information retrieval in web crawling: A survey. In Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI’16). IEEE.

[38]

M. Golfarelli and S. Rizzi. 2018. From Star Schemas to Big Data: 20 Years of Data Warehouse Research. Springer, Berlin.

[39]

B. Abutahoun, M. Alasasfeh, and S. Fraihat. 2019. A framework of business intelligence solution for real estates analysis. In Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems. 1–9.

Digital Library

[40]

A. Cuzzocrea. 2015. Data warehousing and OLAP over big data: a survey of the state-of-the-art, open problems and future challenges. Int. J. Bus. Process Integr. Manage. 7, 4 (2015), 372–377.

[41]

S. A. M. Daradi, U. K. Yusof, and N. I. B. A. Kader. 2018. Prediction of housing price index in malaysia using optimized artificial neural network. Adv. Sci. Lett. 24, 2 (2018), 1307–1311.

[42]

R. Turvey. 2017. The Economics of Real Property: An Analysis of Property Values and Patterns of Use. Routledge.

[43]

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal. 2016. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.

Digital Library

[44]

T. Hastie, R. Tibshirani, and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.

Cited By

Perales-Domínguez CSánchez-Calle JLévano-Rodriguez DGallegos-Carrillo K(2024)Metodologías para la construcción de soluciones de inteligencia de negociosMethodologies for the construction of business intelligence solutionsRevista científica de sistemas e informática10.51252/rcsi.v4i1.6124:1(e612)Online publication date: 10-Jan-2024
https://doi.org/10.51252/rcsi.v4i1.612
Bosse RJino Mde Franco Rosa F(2024)A Study on Data Quality and Analysis in Business IntelligenceITNG 2024: 21st International Conference on Information Technology-New Generations10.1007/978-3-031-56599-1_33(249-253)Online publication date: 9-Jul-2024
https://doi.org/10.1007/978-3-031-56599-1_33
Fraihat SInnab HMakhadmeh SAl-Betar M(2022)Business Intelligence System for Human Resource Management System2022 International Conference on Emerging Trends in Computing and Engineering Applications (ETCEA)10.1109/ETCEA57049.2022.10009861(1-6)Online publication date: Nov-2022
https://doi.org/10.1109/ETCEA57049.2022.10009861
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Published In

cover image Journal of Data and Information Quality

Journal of Data and Information Quality Volume 13, Issue 2

June 2021

132 pages

ISSN:1936-1955

EISSN:1936-1963

DOI:10.1145/3460501

Editor:
Tiziana Catarci
Sapienza University of Rome, Rome, Italy

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 June 2021

Accepted: 01 September 2020

Revised: 01 September 2020

Received: 01 March 2020

Published in JDIQ Volume 13, Issue 2

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Cited By

Perales-Domínguez CSánchez-Calle JLévano-Rodriguez DGallegos-Carrillo K(2024)Metodologías para la construcción de soluciones de inteligencia de negociosMethodologies for the construction of business intelligence solutionsRevista científica de sistemas e informática10.51252/rcsi.v4i1.6124:1(e612)Online publication date: 10-Jan-2024
https://doi.org/10.51252/rcsi.v4i1.612
Bosse RJino Mde Franco Rosa F(2024)A Study on Data Quality and Analysis in Business IntelligenceITNG 2024: 21st International Conference on Information Technology-New Generations10.1007/978-3-031-56599-1_33(249-253)Online publication date: 9-Jul-2024
https://doi.org/10.1007/978-3-031-56599-1_33
Fraihat SInnab HMakhadmeh SAl-Betar M(2022)Business Intelligence System for Human Resource Management System2022 International Conference on Emerging Trends in Computing and Engineering Applications (ETCEA)10.1109/ETCEA57049.2022.10009861(1-6)Online publication date: Nov-2022
https://doi.org/10.1109/ETCEA57049.2022.10009861
Al-Omoush RFraihat SAl-Naymat GAwad M(2022)Design and Implementation of Business Intelligence Framework for a Global Online Retail Business2022 International Conference on Emerging Trends in Computing and Engineering Applications (ETCEA)10.1109/ETCEA57049.2022.10009688(1-6)Online publication date: Nov-2022
https://doi.org/10.1109/ETCEA57049.2022.10009688
Moreno CRodriguez CPuente FPetrlik ILezama PPomachagua Y(2022)Business Intelligence Architecture to Improve Decision Making2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN56167.2022.10008297(7-15)Online publication date: 4-Dec-2022
https://doi.org/10.1109/CICN56167.2022.10008297

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