IP 3 Continuation of IP1 and IP 2

Designing Business Intelligence System with PageRank Algorithm 5

Designing Business Intelligence System with PageRank Algorithm

Dinesh Kalla

CS 857 : Business Intelligence : IP 2

Colorado Technical University

Dr D Revenaugh

17th March 2021

Problem Statement

Organizations are continuously bombarded with vast amounts of data. Managers must gain the expertise needed to fully utilize the benefits that come with business intelligence systems (Olszak, 2016). Data mining has become an essential tool for managers that provide insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships (Visinescu, 2017). Additionally, managers require business intelligence systems that can rank the output in the order of priority. PageRank algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review (Florescu, 2017). The purpose of this research paper is to demonstrate how the PageRank algorithm can be implemented in business intelligence (BI) systems to support managers in decision-making in hiring active authors or researchers belongs to computer science field of study by displaying query results in order of their significance in supporting hiring processes (Kanakia, 2019).

Justification / Literature Review

Algorithms are used in business intelligence systems to unearth patterns otherwise challenging to deduce from a data warehouse. The algorithms can generate several relationships between data and another variable. Most BI systems utilize statistical techniques to analyze and create reports used by managers to make decisions (Arnott, 2017). Examples of these algorithms include; regression analysis, decision trees, association, and cluster analysis.

Companies such as Oracle, Microsoft dynamic, and SAP ERP software have business intelligence functionality (Gounder et al., 2016). SAS, a statistical tool, currently has business intelligence functionalities. Additionally, SQL Server 2005/2008 also contains business intelligence functionality to help users sieve specific information from the database server. Business intelligence systems offer more advanced reporting features than traditional statistical software; therefore, managers find it more intriguing to use data mining tools to provide reports in sequential order to support their decision-making process (Tvrdikova, 2017). As managers grapple with massive data that they use to make a decision, there is a need to rank the reports generated by BI systems based on their significant impact on business processes (Kasemsap, 2016). To have a competitive advantage and effectively run a business, there a need to rely on automated data mining tools that can support their daily activities (Oussous, 2018)

Methodology

In my research, I will use quantitative measures to establish which algorithms are used in various business intelligence systems and how effective they are in supporting managers' decision-making (Arnott, 2017). Face-to-face interviews will be conducted and questionnaires to managers of various organizations to ascertain their satisfaction and the general performance of the BI systems. I will study, compare and analyze different algorithms and their effectiveness in the decision-making in hiring process.

References

Arnott, D., Lizama, F., & Song, Y. (2017). Patterns of business intelligence systems use in organizations. Decision Support Systems, 97, 58-68.

Florescu, C., & Caragea, C. (2017, February). A position-biased pagerank algorithm for keyphrase extraction. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).

Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., … Uzzi, B. (2018). Science of science. Science, 359(6379).

Gounder, M. S., Iyer, V. V., & Al Mazyad, A. (2016, March). A survey on business intelligence tools for university dashboard development. In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC) (pp. 1-7). IEEE.

Kasemsap, K. (2016). The fundamentals of business intelligence. International Journal of Organizational and Collective Intelligence (IJOCI), 6(2), 12-25.

Kanakia, A., Shen, Z., Eide, D., & Wang, K. (2019). A Scalable Hybrid Research Paper Recommender System for Microsoft Academic. In The World Wide Web Conference on (pp. 2893–2899).

Olszak, C. M. (2016). Toward better understanding and use of Business Intelligence in organizations. Information Systems Management, 33(2), 105-123.

Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431-448.

Tvrdikova, M. (2017, June). Support of decision-making by business intelligence tools. In 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07) (pp. 364-368). IEEE.

Visinescu, L. L., Jones, M. C., & Sidorova, A. (2017). Improving decision quality: the role of business intelligence. Journal of Computer Information Systems, 57(1), 58-66.