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Collaborative Business Intelligence on the Cloud

by Mai Kasem, Ehab E. Hassanein, Hesham H. M. Hassan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 12
Year of Publication: 2020
Authors: Mai Kasem, Ehab E. Hassanein, Hesham H. M. Hassan
10.5120/ijca2020920578

Mai Kasem, Ehab E. Hassanein, Hesham H. M. Hassan . Collaborative Business Intelligence on the Cloud. International Journal of Computer Applications. 175, 12 ( Aug 2020), 5-16. DOI=10.5120/ijca2020920578

@article{ 10.5120/ijca2020920578,
author = { Mai Kasem, Ehab E. Hassanein, Hesham H. M. Hassan },
title = { Collaborative Business Intelligence on the Cloud },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2020 },
volume = { 175 },
number = { 12 },
month = { Aug },
year = { 2020 },
issn = { 0975-8887 },
pages = { 5-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number12/31503-2020920578/ },
doi = { 10.5120/ijca2020920578 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:24:49.736321+05:30
%A Mai Kasem
%A Ehab E. Hassanein
%A Hesham H. M. Hassan
%T Collaborative Business Intelligence on the Cloud
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 12
%P 5-16
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Business Intelligence (BI) solutions help organizations make strategic, informed, and effective decisions via analysing and reporting the organisation’s data; the better the data quality, the more accurate and informative the reports are. However, in the real-world complex business landscape, the data from a single organization may not be enough to generate an informed and effective strategic decision. Furthermore, most of the traditional BI solutions are built to serve a single organization; they use the organization’s local data to generate decisions, which could lead to incomplete or non-holistic results leading to non-accurate decisions. Collaborative Business Intelligence (CBI) solutions resolve this challenge by extending the decision-making process beyond the organization’s boundaries. One of the technologies that can make the CBI solutions more accessible is Cloud Computing. Binding two technologies, such as Business Intelligence and Cloud Computing, helps in extending the CBI solutions to reach more users via cloud accessible services. It could also simplify the way different organizations are connected, and the way the data sharing is governed. This paper introduces a new framework called Collaborative Business Intelligence on the Cloud (CBIC). It utilizes the use of CBI solutions and Cloud Computing service to enable users – who have a data sharing agreement – to connect their data warehouses through a cloud BI service and help them run business intelligence functionalities on their data.

References
  1. Rizzi, S., 2012. Collaborative business intelligence. In Business Intelligence (pp. 186-205). Springer Berlin Heidelberg.
  2. Matei, G., 2010. “A collaborative approach of Business Intelligence systems”, Journal of Applied Collaborative Systems, Vol. 2, No. 2.
  3. Steelcase WorkSpace Futures, 2010, How the workplace can improve collaboration.
  4. Scholten, K. and Schilder, S., 2015. The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal.
  5. Mihaela Muntean, 2012, “Theory and Practice in Business Intelligence”, West University of Timisoara, Faculty of Economics and Business Administration, Department of Business Information Systems.
  6. Kaufmann, J. and Chamoni, P., 2014, January. Structuring collaborative business intelligence: A literature review. In 2014 47th Hawaii International Conference on System Sciences (pp. 3738-3747). IEEE.
  7. Rabelo, R.J., 2008. Advanced collaborative business ICT infrastructures. In Methods and Tools for collaborative networked organizations (pp. 337-370). Springer, Boston, MA.
  8. Stefanovic, N., 2015. Collaborative predictive business intelligence model for spare parts inventory replenishment. Comput. Sci. Inf. Syst., 12(3), pp.911-930.
  9. Berthold, H., Rösch, P., Zöller, S., Wortmann, F., Carenini, A., Campbell, S., Bisson, P. and Strohmaier, F., 2010, March. An architecture for ad-hoc and collaborative business intelligence. In Proceedings of the 2010 EDBT/ICDT Workshops (pp. 1-6).
  10. “Collaborative Business Intelligence: The Road to Better Decision Making”, https://plastergroup.com/collaborative-business-intelligence/, viewed June 2020.
  11. Wrembel, R. ed., 2006. Data Warehouses and OLAP: Concepts, Architectures and Solutions: Concepts, Architectures and Solutions. Igi Global.
  12. Gatziu, S., 1999. Data Warehousing: concepts and mechanisms. In Wirtschaftsinformatik als Mittler zwischen Technik, Ökonomie und Gesellschaft (pp. 61-69). Vieweg+ Teubner Verlag.
  13. Bhatia, P., 2019. Data mining and data warehousing: principles and practical techniques. Cambridge University Press.
  14. Lenzerini, M., 2002, June. Data integration: A theoretical perspective. In Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (pp. 233-246). ACM.
  15. Halevy, A.Y., 2001. Answering queries using views: A survey. The VLDB Journal, 10(4), pp.270-294.
  16. Levy, A.Y., Mendelzon, A.O., Sagiv, Y. and Srivastava, D., 1995, May. Answering queries using views. In PODS (Vol. 95, pp. 95-104).
  17. Yuvraj Singh Gurjar & Vijay Singh Rathore, 2013, “Cloud Business Intelligence – Is What Business Need Today”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-1, Issue-6.
  18. Kasem, M. and Hassanein, E.E., 2014. “Cloud Business intelligence survey”. International Journal of Computer Applications, 90(1).
  19. Rajagopalan, V. and Jayasingh, S., 2019. Business Intelligence and Cloud Computing: Benefits, Challenges, and Trends. In Global Virtual Enterprises in Cloud Computing Environments (pp. 1-18). IGI Global.
  20. Rajagopalan, V. and Jayasingh, S., 2019. Business Intelligence and Cloud Computing: Benefits, Challenges, and Trends. In Global Virtual Enterprises in Cloud Computing Environments (pp. 1-18). IGI Global.
  21. Golfarelli, M., Mandreoli, F., Penzo, W., Rizzi, S. and Turricchia, E., 2012. “Business Intelligence Networks”.
  22. Golfarelli, M., Mandreoli, F., Penzo, W., Rizzi, S. and Turricchia, E., 2010, October. Towards OLAP query reformulation in peer-to-peer data warehousing. In Proceedings of the ACM 13th international workshop on Data warehousing and OLAP (pp. 37-44). ACM.
  23. Liu, Y., 2007. The long-term impact of loyalty programs on consumer purchase behavior and loyalty. Journal of marketing, 71(4), pp.19-35.
  24. Reinartz, W.J., 2006. Understanding customer loyalty programs. In Retailing in the 21st Century (pp. 361-379). Springer, Berlin, Heidelberg.
  25. Kucklick, J.P., Kamm, M., Schneider, J. and Vom Brocke, J., 2020, January. Extending Loyalty Programs with BI Functionalities. In Proceedings of the 53rd Hawaii International Conference on System Sciences.
  26. Pinto, C.S., Jayadianti, H., Nugroho, L.E. and Santosa, P.I., 2012. Solving problems of data heterogeneity, semantic heterogeneity and data inequality: an approach using ontologies. In MCIS2012-The 7th Mediterranean Conference on Information Systems, In Knowledge and Technologies in Innovative Information Systems.
  27. Ram, S. and Park, J., 2004. Semantic Conflict Resolution Ontology (SCROL): An ontology for detecting and resolving data and schema-level semantic conflicts. IEEE Transactions on Knowledge and Data engineering, 16(2), pp.189-202.
  28. Ismail, W.S., Sultan, T.I., Nasr, M.M. and Khedr, A.E., 2013. Semantic Conflicts Reconciliation as a Viable Solution for Semantic Heterogeneity Problems. IJACSA) International Journal of Advanced Computer Science and Applications, 4(4).
  29. Mehlhorn, K., 2013. Data structures and algorithms 1: Sorting and searching (Vol. 1). Springer Science & Business Media.
  30. Mehlhorn, K., 2012. Data structures and algorithms 2: graph algorithms and NP-completeness (Vol. 2). Springer Science & Business Media.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Business Intelligence Collaborative Business Intelligence.

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