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Mining association rules for identifying critical factors affecting the implementation of business intelligence systems through WST-WFIM algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 9، دوره 14، شماره 8، آبان 2023، صفحه 83-94 اصل مقاله (401.94 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.28571.3929 | ||
| نویسندگان | ||
| Saeed Nalousi1؛ Yousef Farhang* 2؛ Amin Babazadeh Sangar1؛ Kambiz Majidzadeh1 | ||
| 1Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 57169-63896, Iran. | ||
| 2Department of IT and Computer Engineering, Khoy Branch, Islamic Azad University, Khoy 58135-175, Iran | ||
| تاریخ دریافت: 08 آذر 1400، تاریخ بازنگری: 02 بهمن 1400، تاریخ پذیرش: 17 بهمن 1400 | ||
| چکیده | ||
| In spite of the various advantages of Business Intelligence Systems (BIS), implementing them brings different challenges. Implementing BIS without considering the related challenges and determinants will increase the total cost and decrease added value for the organization. In this study, a questionnaire is developed to identify the critical factors affecting the implementation of BIS in automotive parts manufacturing companies and analyzed through a data mining technique, namely association rules, and the WST-WFIM algorithm on weighted data. The algorithm aims to extract sets of frequent rules and their weights (determined according to their importance) obtained from an expert panel. Taguchi method was adapted to design the algorithm's optimized parameters in order to obtain more effective rules. After applying the new algorithm to effective weighted factors, the relation and importance of each collection of effective factors are analyzed. The findings showed that, from the experts' viewpoint, the most important factors for successful implementation of BIS include (1) Removing potential negative resistances and barriers in spite of the various advantages of the Business Intelligence Systems (BIS), implementing them brings different challenges. Implementing BIS without considering the related challenges and determinants will increase the total cost and decrease added value for the organization. In this study, a questionnaire is developed to identify the critical factors affecting the implementation of BIS in automotive parts manufacturing companies and analyzed through a data mining technique, namely association rules, and the WST-WFIM algorithm on weighted data. The algorithm aims to extract sets of frequent rules and their weights (determined according to their importance) obtained from an expert panel. Taguchi method was adapted to design the algorithm's optimized parameters in order to obtain more effective rules. After applying the new algorithm to effective weighted factors, the relation and importance of each collection of effective factors are analyzed. The findings showed that, from the experts' viewpoint, the most important factors for successful implementation of BIS include (1) Removing potential negative resistances and barriers to implement BIS, (2) alignment between business strategy and BIS characteristics; and (3) system reliability, flexibility, and scalability. implement BIS, (2) alignment between business strategy and BIS characteristics; and (3) system reliability, flexibility, and scalability. | ||
| کلیدواژهها | ||
| Business Intelligence؛ Data Mining؛ Weighted Association Rules؛ WST-WFIM Algorithm؛ Taguchi method | ||
| مراجع | ||
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[1] R. Agrawal and R. Srikant, Fast algorithms for mining association rules, Proc. 20th Int. Conf. Very Large Data Bases, VLDB, Citeseer, 1994. [2] F.A. Amani and A.M. Fadlalla, Data mining applications in accounting: A review of the literature and organizing framework, Int. J. Account. Inf. Syst. 24 (2017), 32–58. [3] M. L. Antonie and O.R. Zaıane, Mining positive and negative association rules: An approach for confined rules, Eur. Conf. Principles of Data Mining and Knowledge Discovery, 2004. [4] E. Ayoubi and S. Aljawarneh, Challenges and opportunities of adopting business intelligence in SMEs: collaborative model, Proc. First Int. Conf. Data Sci. E-learn. Inf. Syst., 2018. [5] M.K. Chen and S.C. Wang, The use of a hybrid fuzzy-Delphi-AHP approach to develop global business intelligence for information service firms, Expert Syst. Appl. 37 (2010), no. 11, 7394–7407. [6] N. Dedic and C. Stanier, Measuring the success of changes to existing business intelligence solutions to improve business intelligence reporting, Int. Conf. Res. Practical Issues of Enterprise Inf. Syst., 2016. [7] A. Fathali, Examining the impact of competitive strategies on corporate innovation: An empirical study in automobile industry, Int. J. Asian Soc. Sci.6 (2016), no. 2, 135–145. [8] A. Ghorbani and S. Farzai, Fraud detection in automobile insurance using a data mining based approach, Int. J. Mechatron. Elekt. Comput. Technol. 8 (2018), no. 27, 3764–3771. [9] J. Han, M. Kamber and D. Mining, Concepts and techniques, Morgan Kaufmann 340 (2006), 94104–3205. [10] J. Han, J. Pei and Y. Yin, Mining frequent patterns without candidate generation, ACM Sigmod Record 29 (2000), no. 2, 1–12. [11] A.S.A. Kadir, A.A. Bakar and A.R. Hamdan, Frequent absence and presence itemset for negative association rule mining, 11th Int. Conf. Intel. Syst. Design Appl., 2011. [12] X. Lai, S. Zhang, N. Mao, J. Liu and Q. Chen, Kansei engineering for new energy vehicle exterior design: An internet big data mining approach, Comput. Indust. Engin. 165 (2022), 107913. [13] X. Li, Y. Fei, T.E. Rizzuto and F. Yang, What are the occupational hazards of construction project managers: A data mining analysis in China, Safety Sci. 134 (2021), 105088. [14] Y.H. Lin, K.M. Tsai, W.J. Shiang, T.C. Kuo and C.H. Tsai, Research on using ANP to establish a performance assessment model for business intelligence systems, Expert Syst. Appl. 36 (2009), no. 2, 4135–4146. [15] I. Martin-Rubio, J. Fombellida and D. Andina, The evolution of business intelligence with neuroinformatics, Int. Conf. Indust. Engin. Oper. Manag., 2018. [16] A. Memarnezhad, S. Hosseini and S. Khatabi, Evaluation of Structure and Performance of Automobile Industry in Iran, Quart. J. Econ. Model. 3 (2010), no. 4, 103–120. [17] A. Mikroyannidis and B. Theodoulidis, Ontology management and evolution for business intelligence, Int. J. Inf. Manag. 30 (2010), no. 6, 559–566. [18] M. Nakayama, O. Isik, N. Sutcliffe and S. Olbrich, Grassroots Business Intelligence as an Enabler of Change Management: A Case Study at a Large Global Manufacturing Firm, Complex Syst. Inf. Model. Quart. 23 (2020), 1–11. [19] S. Nanda, B.P. Bhol and S. Misra, Business intelligence and decision making influence Bancassurance system, Odisha J. Soc. Sci. 7 (2020), no. 1. [20] J. Park, S. Lee, C. Oh and B. Choe, A data mining approach to deriving safety policy implications for taxi drivers, J. Safety Res. 76 (2021), 238–247. [21] J. Reinschmidt and A. Francoise, Business intelligence certification guide, IBM International Technical Support Organisation, 2000. [22] P. Rikhardsson and O. Yigitbasioglu, Business intelligence & analytics in management accounting research: Status and future focus, Int. J. Account. Inf. Syst. 29 (2018), 37–58. [23] J. Rong, H.Q. Vu, R. Law and G. Li, A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining, Tourism Manag. 33 (2012), no. 4, 731–740. [24] B. Sahay and J. Ranjan, Real time business intelligence in supply chain analytics, Inf. Manag. Comput. Secur. 16 (2008), no. 1, 28–48. [25] M. Seah, M.H. Hsieh and P.D. Weng, A case analysis of Savecom: The role of indigenous leadership in implementing a business intelligence system, Int. J. Inf. Manag. 30 (2010), no. 4, 368–373. [26] R. Sethi and B. Shekar, Subjective interestingness in association rule mining: A theoretical analysis, Digital Business, Springer, 2019. [27] W. Yeoh and A. Koronios, Critical success factors for business intelligence systems, J. Comput. Inf. Syst. 50 (2010), no. 3, 23-32. [28] W. Yeoh and A. Popoviˇc, Extending the understanding of critical success factors for implementing business intelligence systems, J. Assoc. Inf. Sci. Technol. 67 (2016), no. 1, 134–147. | ||
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