Oracle database query
By Brendan Tierney, Oracle Ace Director
One of the new SQL features introduced into the Oracle 12c Database is the ability to create on the fly predictive models for your data. All you need to do is to write a SQL query that will build a predictive model and will then apply this model to your data. This is all done in one step and does not require you have knowledge of its inner works. The predictive models that are built during the execution of the Predictive Query only exists while the query is being run. When the query is finished all the models and associated settings are deleted. These are called transient models.
One of the confusing aspects of the new feature is that is goes by many names. In the documentation it can be found under On-the-Fly Models, and sometimes it is called Dynamic Queries. This feature is also part of the Oracle Data Miner tool that is part of SQL and here it goes by the name of Predictive Queries.
On-the-Fly Models = Dynamic Queries = Predictive Queries. Yes that is a bit confusing. From talking to some of the Oracle folks is seems that the name Predictive Queries might be the name they will be using going forward.
What are Predictive Queries And Why Would You Need Them?
Predictive Queries enable you to build and score data quickly using the in-database data mining algorithms, without the complexity of needing to understand the required settings and fine-tuning of the models. All models created during the execution of the Predictive Query will not exist once the Predictive Query is finished executing. You cannot inspect or tune the algorithms being used or the models that are generated. So you have to trust what Oracle is doing behind the scenes. If you are a Data Scientist type of person you will typically want to tune the models, so this approach is maybe not for you. But if you want to very quickly build models and score your data then Predictive Queries is something you should consider.
A major advantage of using Predictive Queries is that you can partition the data so that predictive models can be build specific to each partition. What this will do, is that it will divide your data into the relevant partitions and will then create a predictive model specific to that partition and then score the data in the partition. Typically for most data mining tools you will have to specifically define the creation of the data subset for the partition, define how to build the model and then run the model to score the data. All of these steps have to be manually defined. By defining a partition in the Predictive Query all of these work will be done automatically for you. There are two major advantages to this. The first is that as new data partitions (i.e. a new value exists for the partition attribute) are created the Predictive Query will automatically pick this up and do all the work for you. The second is that Predictive Queries allows you to use the Parallel Query option to speed up the process of scoring the data. This will be particularly useful when you are working with Big Data.
Using Predictive Queries is very similar to using the PREDICT procedure in the DMBS_PREDICTIVE_ANALYTICS PL/SQL package, except that instead of using PL/SQL, Predictive Queries uses SQL.
There are two ways you can go about creating your predictive queries. The first option is to write a SQL statement to prompt the database to use the in-database data mining algorithms. The second (and much easier) option is to use the Predictive Queries nodes in the Oracle Data Miner tool.
The following sections of this article will illustrate how to build Predictive Queries using Oracle Data Miner and using SQL.
Building Predictive Queries Using Oracle Data Miner
The Predictive Queries option in the Oracle Data Miner GUI, that is part of SQL Developer, will only become visible when you are connected to an Oracle 12c Database. You will see the Predictive Queries section under the Components windows, as shown in Figure 1.
Figure 1 : Predictive Queries section of the Components Window
There are four types of Predictive Query Nodes available in Oracle Data Miner.
Predictive Queries Node
Anomaly Detection Node
The input data set will be used to build an anomaly model and this model will then be applied to the same input data set.
Clusters will be automatically detected and the input data set will be returned with an assigned cluster for each record.
Feature Extraction Query
This can be used to automatically identify and extract key features from the input data set.
The Predictive Query node can be used to perform classification or regression on the input data set. For classification the target attribute needs to have a character data type. For Regression the data type should be numeric.
To illustrate how to build and use a Predictive Query node in the Oracle Data Miner tool, the following example will use the Predictive Query node to build and apply a Classification model.
To create a Data Source and Predictive Query nodes on your workflow you need to:
1. Create and Define a Data Source Node for the table that contains the data. The Data Source Node is located in the Data section of the Components window.
2. Create the Predictive Query node on your workflow.
3. Connect the Data Source node to the Predictive Query node. To do this right click on the Data Source node, select Connect from the menu, move the mouse to the Predictive Query node and click the mouse again. A gray arrow will now appear between the two nodes. This means they are connected.
To define the settings for the Predictive Query node you will need to:
1. Open the Predictive Query node by double clicking on the node.
2. The Edit Predictive Query node window will open. This window have 4 tabs/sections that allows you to define the Predictions to be made, how the data will be Partitioned (and hence the number of Predictive Query models that will be created), the Input list of attributes and the Output list of attributes.
3. Define the Predictions.
a) Define the Case ID for the input data set. The Case ID can be the primary key or some attribute that gives uniqueness to each attribute. The Case ID can be left null, but by defining the Case ID allows for the repeatability of the process and outputs. In our sample data set the Case ID is CUST_ID.
b) Define the target attribute for the data set. Click on the green plus icon to open the Add Target window. In this window select the required Target attribute. In our data set this is the AFFINITY_CARD attribute.
c) Define the Data Mining Type. This will be either Classification or Regression. To define the Data Mining Type you will need to click on the icon under the Data Mining Type column. A drop down list will appear that lists Categorical or Numerical. In our scenario we want to perform Classification so you will need to select Categorical.What is the meaning of izuku? How to get cancer? how to call a helper function inside of another function Oh what a night lyrics meaning? How to take care of a bamboo plant? How to drop an engine into a car tips? Safety tips when building a computer? What are delts? What language does hungary speak? What is the world's record for the longest time someone has held their breath underwater? How to transfer photos from iphone to iphone? Tips on how can we reduce text in a powerpoint presentation? How to take off acrylics without acetone? What does variation mean in skateboarding tricks? How to win mancala? What is the meaning of hydrology? 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