Oracle 12c Advanced Analytics Option new features
With the release of Oracle 12c (finally) now have a lot of learning to do. Oracle 12c is a different beast to what we have been used to up to now.
As part of the 12c there are a number of new in-database Advanced Analytics features. These are separate to the Advanced Analytics new features that come as part of the Oracle Data Miner tool, that is part of SQL Developer.
This post will only look at the new features that are part of the 12c Database. The new in-Database Advanced Analytics features include:
- Using Decisions Trees for Text analysis is now possible. Up to now (11.2g) when you wanted to do text classification you had to exclude Decision Trees from the process. This was because the Decision Trees algorithm could not support nested data.
- Additionally for text mining some of the text processing has been moved from having a separate step, to being part of the some of the algorithms.
- A number of additional features are available for Clustering. These include a cluster distance (from the centroid) and details functions.
- There is a new clustering algorithm (in addition to the K-Means and O-Cluster algorithms), called Expectation Maximization algorithm. This creates a density model that can be give better results when data from different domains are combined for clustering. This algorithm will also determine the optimal number of clusters.
- There are two new Feature Extraction methods that are scalable for high dimensional data, large number of records, for both structured and unstructured. This can be used to reduce the number of dimensions to use as input to the data mining algorithms. The first of these is called Singular Value Decomposition (SVD) and is widely used in text mining. The second method can be considered a special scoring method of SVD is called Principal Component Analysis (PCA). With this method it produces projections that are scaled with the data variance.
- A new feature of the GLM algorithm is that it will perform a feature section step. This is used to reduce the number of predictors used by the algorithm and allow for faster builds. This will makes the outputs more understandable and model more transparent. This feature is not default so you will need to set this on if you want to use it with the GLM algorithm.
- In previous versions of the database, there could be some performance issues that relate to the data types used. In 12c these has been addressed for BINARY_DOUBLE and BINARY_FLOAT. So if you are using these data types you should now see faster scoring of the data in 12c
- There is new in-database feature called Predictive Queries. This allows on-the-fly models that are temporary models that are formed as part of an analytics clause. These models cannot be tuned and you cannot see the details of the model produced. They are formed for the query and do not exist afterwards.
SELECT cust_id, age, pred_age, age-pred_age age_diff, pred_det FROM
(SELECT cust_id, age, pred_age, pred_det,
RANK() OVER (ORDER BY ABS(age-pred_age) DESC) rnk FROM
(SELECT cust_id, age,
PREDICTION(FOR age USING *) OVER () pred_age,
PREDICTION_DETAILS(FOR age ABS USING *) OVER () pred_det
WHERE rnk <= 5;
These are the new in-database Advanced Analytics (Data Mining) features. Apart from the new algorithms or changes to them, most of the other changes gives greater transparency into what the algorithms/models are doing. This is good as it allows us to better understand and see what is happening.
The rest of the new Advanced Analytics Option new features will be part of Oracle Data Miner tool in SQL Developer 4. My next blog post will cover the new features in SQL Developer 4.
I haven’t mentioned anything about ORE. The reason for that is that it comes as a separate install and its current version 1.3 works the same in 184.108.40.206g as well as 12c. I’ve had some previous blog posts on this and you can check out the ORE website on OTN.