supportability and security. With Big Data Connectors, the solution is tightly integrated with Oracle Exadata and Oracle Database, so you can analyze all your data together with extreme performance.
Once data has been loaded from Oracle Big Data Appliance into Oracle Database or Oracle Exadata, end users can use one of the following easy-to-use tools for in-database, advanced analytics:
Oracle R Enterprise – Oracle’s version of the widely used Project R statistical environment enables statisticians to use R on very large data sets without any modifications to the end user experience. Examples of R usage include predicting airline delays at a particular airports and the submission of clinical trial analysis and results.
In-Database Data Mining – the ability to create complex models and deploy these on very large data volumes to drive predictive analytics. End-users can leverage the results of these predictive models in their BI tools without the need to know how to build the models. For example, regression models can be used to predict customer age based on
purchasing behavior and demographic data.
In-Database Text Mining – the ability to mine text from micro blogs, CRM system comment fields and review sites combining Oracle Text and Oracle Data Mining. An example of text mining is sentiment analysis based on comments. Sentiment analysis tries to show how customers feel about certain companies, products or activities.
In-Database Semantic Analysis – the ability to create graphs and connections between various data points and data sets. Semantic analysis creates, for example, networks of relationships determining the value of a customer’s circle of friends. When looking at customer churn customer value is based on the value of his network, rather than on just
the value of the customer.
In-Database Spatial – the ability to add a spatial dimension to data and show data plotted on a map. This ability enables end users to understand geospatial relationships and trends much more efficiently. For example, spatial data can visualize a network of people and their geographical proximity. Customers who are in close proximity can
readily influence each other’s purchasing behavior, an opportunity which can be easily missed if spatial visualization is left out.
In-Database MapReduce – the ability to write procedural logic and seamlessly leverage Oracle Database parallel execution. In-database MapReduce allows data scientists to create high-performance routines with complex logic.
In-database MapReduce can be exposed via SQL. Examples of leveraging in-database MapReduce are sessionization of weblogs or organization of Call Details Records (CDRs).