Business intelligence systems give important and competitive information to business planners and decision-makers by combining operational and historical data with analytical tools. Business intelligence (BI) aims to increase the timeliness and quality of data, allowing managers to better comprehend their company's position with rivals. For example, changes in market share, consumer behavior and spending patterns, customer preferences, corporate capabilities, and market circumstances may be analyzed using business intelligence tools and technology. In addition, analysts and managers may utilize business intelligence to determine which changes are most likely to adapt to shifting trends. The nontrivial extraction of implicit, previously unknown, and possibly beneficial information from data is known as data mining. Clustering, data summarization, learning classification rules, discovering dependency networks, analyzing changes, and detecting anomalies are all examples of technological techniques. The introduction of the data warehouse as a repository, advancements in data purification, better hardware and software capabilities, and the emergence of web architecture have all combined to produce a richer business intelligence environment than previously accessible. This document tries to give a framework for developing a business intelligence system. AI has been used to find and investigate security flaws. Manipulation and movement When given a limited static environment, AI robots can readily detect and map their surroundings.
Published in | American Journal of Artificial Intelligence (Volume 7, Issue 1) |
DOI | 10.11648/j.ajai.20230701.14 |
Page(s) | 24-30 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Business Intelligence, Artificial Intelligence, Big Data
[1] | Elfving, J., & Lemoine, K. (2012). Exploring the concept of Customer Relationship Management: emphasizing social. In. |
[2] | Foshay, N., Taylor, A., & Mukherjee, A. (2014). Winning the hearts and minds of business intelligence users: The role of metadata. Information systems management, 31 (2), 167-180. |
[3] | Khan, R. A., & Quadri, S. (2012). Business intelligence: an integrated approach. Business Intelligence Journal, 5 (1), 64-70. |
[4] | Krishna, C., & Rohit, H. (2018). A review of Artificial Intelligence methods for data science and data analytics: Applications and Research Challenges. 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, |
[5] | Kudyba, S., & Hoptroff, R. (2001). Data mining and business intelligence: A guide to productivity. Igi Global. |
[6] | MacGillivray, A. E. (2001). Using Business Intelligence (information Technology) Tools to Facilitate Front-line Priority-setting in a Public Sector Organisation. National Library of Canada= Bibliothèque nationale du Canada, Ottawa. |
[7] | Moolayil, J., Moolayil, J., & John, S. (2019). Learn Keras for deep neural networks. Springer. |
[8] | Nelke, M., & Håkansson, C. (2015). Competitive intelligence for information professionals. Chandos Publishing. |
[9] | Ren, Z., & Wang, D. (2008). Building a Business Intelligence Application with Oracle e-Business Suits 12. 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, |
[10] | Reshi, Y. S., & Khan, R. A. (2014). Creating business intelligence through machine learning: An Effective business decision making tool. Information and Knowledge Management, |
[11] | Shanmuganathan, S. (2016). Artificial neural network modelling: An introduction. In Artificial neural network modelling (pp. 1-14). Springer. |
[12] | Kilanko, V. (2022). Turning Point: Policymaking in the Era of Artificial Intelligence, by Darrell M. West and John R. Allen, Washington, DC: Brookings Institution Press, 2020, 297 pp., hardcover 24.99, paperback 19.99. |
[13] | Kilanko, V. The Transformative Potential of Artificial Intelligence in Medical Billing: A Global Perspective. |
[14] | Mungoli, N. (2023). Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks. arXiv preprint arXiv: 2304.02653. |
[15] | Mungoli, N. (2023). Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models. arXiv preprint arXiv: 2304.03290. |
[16] | Mungoli, N. (2023). Deciphering the Blockchain: A Comprehensive Analysis of Bitcoin's Evolution, Adoption, and Future Implications. arXiv preprint arXiv: 2304.02655. |
[17] | Sahija, D. (2021). Critical review of machine learning integration with augmented reality for discrete manufacturing. Independent Researcher and Enterprise Solution Manager in Leading Digital Transformation Agency, Plano, USA. |
[18] | Sahija, D. (2021). User Adoption of Augmented Reality and Mixed Reality Technology in Manufacturing Industry. Int J Innov Res Multidisciplinary Field Issue, 27, 128-139. |
[19] | Mungoli, N. (2023). Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency. arXiv preprint arXiv: 2304.13738. |
[20] | Mungoli, N. (2020). Exploring the Technological Benefits of VR in Physical Fitness (Doctoral dissertation, The University of North Carolina at Charlotte). |
[21] | Mahmood, T., Fulmer, W., Mungoli, N., Huang, J., & Lu, A. (2019, October). Improving information sharing and collaborative analysis for remote geospatial visualization using mixed reality. In 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 236-247). IEEE. |
[22] | Mughal, A. A. (2018). Artificial Intelligence in Information Security: Exploring the Advantages, Challenges, and Future Directions. Journal of Artificial Intelligence and Machine Learning in Management, 2 (1), 22-34. |
[23] | Mughal, A. A. (2018). The Art of Cybersecurity: Defense in Depth Strategy for Robust Protection. International Journal of Intelligent Automation and Computing, 1 (1), 1-20. |
[24] | Mughal, A. A. (2019). Cybersecurity Hygiene in the Era of Internet of Things (IoT): Best Practices and Challenges. Applied Research in Artificial Intelligence and Cloud Computing, 2 (1), 1-31. |
[25] | Mughal, A. A. (2020). Cyber Attacks on OSI Layers: Understanding the Threat Landscape. Journal of Humanities and Applied Science Research, 3 (1), 1-18. |
[26] | Mughal, A. A. (2019). A COMPREHENSIVE STUDY OF PRACTICAL TECHNIQUES AND METHODOLOGIES IN INCIDENT-BASED APPROACHES FOR CYBER FORENSICS. Tensorgate Journal of Sustainable Technology and Infrastructure for Developing Countries, 2 (1), 1-18. |
[27] | Mughal, A. A. (2022). Building and Securing the Modern Security Operations Center (SOC). International Journal of Business Intelligence and Big Data Analytics, 5 (1), 1-15. |
[28] | Mughal, A. A. (2022). Well-Architected Wireless Network Security. Journal of Humanities and Applied Science Research, 5 (1), 32-42. |
[29] | Mughal, A. A. (2021). Cybersecurity Architecture for the Cloud: Protecting Network in a Virtual Environment. International Journal of Intelligent Automation and Computing, 4 (1), 35-48. |
[30] | Azim, A. Bazzi, R. Shubair and M. Chafii, "Dual-Mode Chirp Spread Spectrum Modulation," in IEEE Wireless Communications Letters, vol. 11, no. 9, pp. 1995-1999, Sept. 2022, doi: 10.1109/LWC.2022.3190564. |
[31] | W. Njima, A. Bazzi and M. Chafii, "DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning," in IEEE Access, vol. 10, pp. 6989669909, 2022, doi: 10.1109/ACCESS.2022.3187837. |
[32] | Azim, A. W., Bazzi, A., Shubair, R. and Chafii, M., 2022. A Survey on Chirp Spread Spectrum-based Waveform Design for IoT. arXiv preprint arXiv: 2208.10274. |
[33] | Azim, A. W., Bazzi, A., Fatima, M., Shubair, R., & Chafii, M. (2022). Dual-Mode Time Domain Multiplexed Chirp Spread Spectrum. arXiv preprint arXiv: 2210.04094. |
[34] | Bazzi, A. and M. Chafii, "On Integrated Sensing and Communication Waveforms with Tunable PAPR," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2023.3250263. |
[35] | Singh, V., & Verma, N. K. (2018). Deep learning architecture for high-level feature generation using stacked auto encoder for business intelligence. In Complex systems: solutions and challenges in economics, management and engineering (pp. 269-283). Springer. |
APA Style
Jasmin Praful Bharadiya. (2023). A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics. American Journal of Artificial Intelligence, 7(1), 24-30. https://doi.org/10.11648/j.ajai.20230701.14
ACS Style
Jasmin Praful Bharadiya. A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics. Am. J. Artif. Intell. 2023, 7(1), 24-30. doi: 10.11648/j.ajai.20230701.14
AMA Style
Jasmin Praful Bharadiya. A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics. Am J Artif Intell. 2023;7(1):24-30. doi: 10.11648/j.ajai.20230701.14
@article{10.11648/j.ajai.20230701.14, author = {Jasmin Praful Bharadiya}, title = {A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics}, journal = {American Journal of Artificial Intelligence}, volume = {7}, number = {1}, pages = {24-30}, doi = {10.11648/j.ajai.20230701.14}, url = {https://doi.org/10.11648/j.ajai.20230701.14}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20230701.14}, abstract = {Business intelligence systems give important and competitive information to business planners and decision-makers by combining operational and historical data with analytical tools. Business intelligence (BI) aims to increase the timeliness and quality of data, allowing managers to better comprehend their company's position with rivals. For example, changes in market share, consumer behavior and spending patterns, customer preferences, corporate capabilities, and market circumstances may be analyzed using business intelligence tools and technology. In addition, analysts and managers may utilize business intelligence to determine which changes are most likely to adapt to shifting trends. The nontrivial extraction of implicit, previously unknown, and possibly beneficial information from data is known as data mining. Clustering, data summarization, learning classification rules, discovering dependency networks, analyzing changes, and detecting anomalies are all examples of technological techniques. The introduction of the data warehouse as a repository, advancements in data purification, better hardware and software capabilities, and the emergence of web architecture have all combined to produce a richer business intelligence environment than previously accessible. This document tries to give a framework for developing a business intelligence system. AI has been used to find and investigate security flaws. Manipulation and movement When given a limited static environment, AI robots can readily detect and map their surroundings.}, year = {2023} }
TY - JOUR T1 - A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics AU - Jasmin Praful Bharadiya Y1 - 2023/06/27 PY - 2023 N1 - https://doi.org/10.11648/j.ajai.20230701.14 DO - 10.11648/j.ajai.20230701.14 T2 - American Journal of Artificial Intelligence JF - American Journal of Artificial Intelligence JO - American Journal of Artificial Intelligence SP - 24 EP - 30 PB - Science Publishing Group SN - 2639-9733 UR - https://doi.org/10.11648/j.ajai.20230701.14 AB - Business intelligence systems give important and competitive information to business planners and decision-makers by combining operational and historical data with analytical tools. Business intelligence (BI) aims to increase the timeliness and quality of data, allowing managers to better comprehend their company's position with rivals. For example, changes in market share, consumer behavior and spending patterns, customer preferences, corporate capabilities, and market circumstances may be analyzed using business intelligence tools and technology. In addition, analysts and managers may utilize business intelligence to determine which changes are most likely to adapt to shifting trends. The nontrivial extraction of implicit, previously unknown, and possibly beneficial information from data is known as data mining. Clustering, data summarization, learning classification rules, discovering dependency networks, analyzing changes, and detecting anomalies are all examples of technological techniques. The introduction of the data warehouse as a repository, advancements in data purification, better hardware and software capabilities, and the emergence of web architecture have all combined to produce a richer business intelligence environment than previously accessible. This document tries to give a framework for developing a business intelligence system. AI has been used to find and investigate security flaws. Manipulation and movement When given a limited static environment, AI robots can readily detect and map their surroundings. VL - 7 IS - 1 ER -