Wednesday Jul 15, 2015
Monday May 04, 2015
By Charlie Berger, Advanced Analytics-Oracle on May 04, 2015
To download, visit:
New Data Miner Features in SQL Developer 4.1
In response to the growing popularity of JSON data and its use in Big Data configurations, Data Miner now provides an easy to use JSON Query node. The JSON Query node allows you to select and aggregate JSON data without entering any SQL commands. The JSON Query node opens up using all of the existing Data Miner features with JSON data. The enhancements include:
Data Source Node
o Automatically identifies columns containing JSON data by identifying those with the IS_JSON constraint.
o Generates JSON schema for any selected column that contain JSON data.
o Imports a JSON schema for a given column.
o JSON schema viewer.
Create Table Node
o Ability to select a column to be typed as JSON.
o Generates JSON schema in the same manner as the Data Source node.
JSON Data Type
o Columns can be specifically typed as JSON data.
JSON Query Node (see related JSON node blog posting)
o Ability to utilize any of the selection and aggregation features without having to enter SQL commands.
o Ability to select data from a graphical layout of the JSON schema, making data selection as easy as it is with scalar relational data columns.
o Ability to partially select JSON data as standard relational scalar data while leaving other parts of the same JSON document as JSON data.
o Ability to aggregate JSON data in combination with relational data. Includes the Sub-Group By option, used to generate nested data that can be passed into mining model build nodes.
o Improved database session management resulting in less database sessions being generated and a more responsive user interface.
o Filter Columns Node - Combined primary Editor and associated advanced panel to improve usability.
o Explore Data Node - Allows multiple row selection to provide group chart display.
o Classification Build Node - Automatically filters out rows where the Target column contains NULLs or all Spaces. Also, issues a warning to user but continues with Model build.
o Workflow - Enhanced workflows to ensure that Loading, Reloading, Stopping, Saving operations no longer block the UI.
o Online Help - Revised the Online Help to adhere to topic-based framework.
Selected Bug Fixes (does not include 4.0 patch release fixes)
o GLM Model Algorithm Settings: Added GLM feature identification sampling option (Oracle Database 12.1 and above).
o Filter Rows Node: Custom Expression Editor not showing all possible available columns.
o WebEx Display Issues: Fixed problems affecting the display of the Data Miner UI through WebEx conferencing.
For More Information and Support, please visit the Oracle Data Mining Discussion Forum on the Oracle Technology Network (OTN)
Return to Oracle Data Miner page on OTN
Thursday Mar 21, 2013
By Charlie Berger, Advanced Analytics-Oracle on Mar 21, 2013
Best Practices using Oracle Advanced Analytics with Oracle Exadata
You need to visit this Oracle Exadata Webcast Main page first and submit your registration information. Then you’ll receive an email so you can view the Webcast. This is external so you can share with anyone can download the presentation as well. FYI. Charlie
Thursday Dec 09, 2010
By Charlie Berger, Advanced Analytics-Oracle on Dec 09, 2010
Sunday Oct 31, 2010
By Charlie Berger, Advanced Analytics-Oracle on Oct 31, 2010
Wednesday Oct 27, 2010
By Charlie Berger, Advanced Analytics-Oracle on Oct 27, 2010
Wednesday May 19, 2010
New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Data Mining
By Charlie Berger, Advanced Analytics-Oracle on May 19, 2010
Monday Mar 08, 2010
Wednesday Feb 24, 2010
Thursday Feb 18, 2010
By Charlie Berger, Advanced Analytics-Oracle on Feb 18, 2010
The America's Cup has been away from U.S. shores for 15 years, the longest drought since 1851. With the challenge of squeezing out every micro-joule of energy from the wind and with the goal of maximizing "velocity made good", the BMW Oracle Racing Team turned to Oracle Data Mining.
"Imagine standing under an avalanche of data - 2500 variables, 10 times per second and a sailing team demanding answers to design and sailing variations immediately. This was the challenge facing the BMW ORACLE Racing Performance Analysis Team every sailing day as they refined and improved their giant 90 foot wide, 115 foot long trimaran sporting the largest hard-sail wing ever made. Using ORACLE DATA MINING accessing an ORACLE DATABASE and presenting results real time using ORACLE APPLICATION EXPRESS the performance team managed to provide the information required to optimise the giant multihull to the point that it not only beat the reigning America's Cup champions Alinghi in their giant Catamaran but resoundingly crushed them in a power display of high speed sailing. After two races - and two massive winning margins - the America's Cup was heading back to America - a triumph for the team, ORACLE and American technology."
--Ian Burns, Performance Director, BMW ORACLE Racing Team
Visit the http://www.sail-world.com/USA/Americas-Cup:-Oracle-Data-Mining-supports-crew-and-BMW-ORACLE-Racing/68834 for pictures, videos and full information.
Wednesday Feb 10, 2010
By Charlie Berger, Advanced Analytics-Oracle on Feb 10, 2010
Saturday Jan 23, 2010
By Charlie Berger, Advanced Analytics-Oracle on Jan 23, 2010
Monday Jan 18, 2010
By Charlie Berger, Advanced Analytics-Oracle on Jan 18, 2010
Here is a quick and simple application for fraud and anomaly detection. To replicate this on your own computer, download and install the Oracle Database 11g Release 1 or 2. (See http://www.oracle.com/technology/products/bi/odm/odm_education.html for more information). This small application uses the Automatic Data Preparation (ADP) feature that we added in Oracle Data Mining 11g. Click here to download the CLAIMS data table. [Download the .7z file and save it somwhere, unzip to a .csv file and then use SQL Developer data import wizard to import the claims.csv file into a table in the Oracle Database.]
First, we instantiate the ODM settings table to override the defaults. The default value for Classification data mining function is to use our Naive Bayes algorithm, but since this is a different problem, looking for anomalous records amongst a larger data population, we want to change that to SUPPORT_VECTOR_MACHINES. Also, as the 1-Class SVM does not rely on a Target field, we have to change that parameter to "null". See http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/anomalies.htm for detailed Documentation on ODM's anomaly detection.
drop table CLAIMS_SET;
create table CLAIMS_SET (setting_name varchar2(30), setting_value varchar2(4000));
insert into CLAIMS_SET values ('ALGO_NAME','ALGO_SUPPORT_VECTOR_MACHINES');
insert into CLAIMS_SET values ('PREP_AUTO','ON');
Then, we run the dbms_data_mining.create_model function and let the in-database Oracle Data Mining algorithm run through the data, find patterns and relationships within the CLAIMS data, and infer a CLAIMS data mining model from the data.
'CLAIMS', 'POLICYNUMBER', null, 'CLAIMS_SET');
After that, we can use the CLAIMS data mining model to "score" all customer auto insurance policies, sort them by our prediction_probability and select the top 5 most unusual claims.
-- Top 5 most suspicious fraud policy holder claims
select * from
(select POLICYNUMBER, round(prob_fraud*100,2) percent_fraud,
rank() over (order by prob_fraud desc) rnk from
(select POLICYNUMBER, prediction_probability(CLAIMSMODEL, '0' using *) prob_fraud
where PASTNUMBEROFCLAIMS in ('2 to 4', 'more than 4')))
where rnk <= 5
order by percent_fraud desc;
Leave these results inside the database and you can create powerful dashboards using Oracle Business Intelligence EE (or any reporting or dashboard tool that can query the Oracle Database) that multiple ODM's probability of the record being anomalous times (x) the dollar amount of the claim, and then use stoplight color coding (red, orange, yellow) to flag only the more suspicious claims. Very automated, very easy, and all inside the Oracle Database!
By Charlie Berger, Advanced Analytics-Oracle on Jan 18, 2010
Thursday Jan 07, 2010
Everything about Oracle Data Mining, a component of the Oracle Advanced Analytics Option - News, Technical Information, Opinions, Tips & Tricks. All in One Place
- NHS Business Services Authority Gains Better Insight into Data, Identifies circa GBP100 Million (US$156 Million) in Potential Savings in Just Three Months
- BIWA 2016 Here are some of our early accepted presentations!!
- Oracle Advanced Analytics at Oracle Open World 2015
- Oracle Advanced Analytics Oracle University (OU) Classes in Cambridge, MA. September 28-Oct. 1, 2015
- Big Data Analytics with Oracle Advanced Analytics: Making Big Data and Analytics Simple white paper
- 2015 BIWA SIG Virtual Conference - Two Days of "Live" Talks by Experts - FREE
- Call for Abstracts at BIWA Summit'16 - The Oracle Big Data + Analytics User Conference
- Oracle Data Miner 4.1, SQL Developer 4.1 Extension Now Available!
- OpenWorld 2015 Call for Proposals Extended to Wed, May 6th, 11:59 p.m
- Use Repository APIs to Manage and Schedule Workflows to run