Affiliation:
1. Uber Technologies, USA
Abstract
The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.
Reference113 articles.
1. Abu-Mustafa, Y., Magdon-Ismail, M., & Lin, H. T. (2012). Learning from data: a short course. AMLbooks.
2. Adams, R. P., & MacKay, D. J. (2007). Bayesian online changepoint detection. arXiv preprint arXiv:0710.3742
3. Ahmad, S., & Purdy, S. (2016). Real-Time Anomaly Detection for Streaming Analytics. arXiv preprint arXiv:1607.02480
4. Applying Support Vector Machines to Imbalanced Datasets
5. Spark SQL
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献