Oracle Machine Learning and Analytic Views

Analytic views and Oracle Machine Learning complement each other by combining dimensional data organization with inductive inference for comprehensive data analysis.

Analytic views organize data using a dimensional model. Analytic views provide a fast and efficient way to create analytic queries of data stored in existing database tables and views. Analytic Views and Oracle Machine Learning are complementary activities.

An analytic view includes navigation, join, aggregation, and calculation rules, thus eliminating the need to include these rules in queries. However, analytic views do not have inductive inference capabilities. Inductive inference, the process of reaching a general conclusion from specific examples, is a characteristic of machine learning. Inductive inference is also known as computational learning.

Analytic views provide a multidimensional view of the data, including support for hierarchies, and analytic view objects. From a business perspective, analytic views offer a way to present the data.

Oracle Machine Learning and analytic views can be used together in a number of ways. Analytic views can be used to analyze machine learning results at different levels of granularity. Machine learning can help you construct more interesting and useful analytic view. For example, the results of predictive machine learning can be added as custom measures to an analytic view or to suggest important attributes. Such measures can provide information such as "likely to default" or "likely to buy" for each customer. Analytic views can then aggregate and summarize the probabilities.