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The Net Takeaway: Mark Rittman on Oracle Data Mining in 10g


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Mark Rittman on Oracle Data Mining in 10g · 06/09/2004 02:56 PM, Analysis Database

We are an Oracle house here, and though my heart lies with MS SQL Server, my brain has learned Oracle. And there are some great things there. The indefatigable Mark Rittman has recently blogged about how impressive the built-in data mining is in Oracle 10g. Based on their purchase of the Darwin product from Thinking Machines (one of the first real commercial data mining tools), Oracle has been building more and more data mining into the core db… but unlike MS, they’ve made it nightmarish to get to. Basically, like their OLAP, Oracle provides no simple client tools; instead, you have to write Java code to get to it. This has made it a no-option for me, especially compared to MS’s GUIs and using Excel as an OLAP client tool.

Luckily, Oracle has now taken a step in the right direction with 10g. Mark points out the following on his entry:

Oracle 9i and 10g have a Data Mining Option for the Enterprise Edition of the database which embeds a number of data mining routines in the database engine. Access to these routines was initially provided in Oracle 9i through a Java data mining API, and when Oracle 10g was released this access was broadened through the introduction of DMBS_DATA_MINING, a PL/SQL API for these routines. Oracle position the data mining option as an embedded data mining engine with the emphasis on real-time scoring and classification of data, the idea being that you build your mining model using DM4J (or any tool such as SPSS that can output the mining model using PMML) and then load it into the database ready to carry out scoring.

(Note that SPSS (using SmartScore) and Clementine both can work with PMML XML model descriptions) and

If you want to have a play around with Oracle data mining, it’s installed alongside the OLAP Option when you choose the ‘Data Warehousing’ DBCA template. There’s a useful ‘Oracle By Example’ tutorial available on OTN entitled Using Oracle Data Mining to Predict Data Behavior which walks through the creation of two data mining models, and the DM4J project page has a number of data mining tutorials that demonstrate the use of DM4J’s data mining wizards.

But the best, the very best, is all the detail he goes into here about the PL/SQL access to the API. Not only does it give access to generate models and score data (as mentioned above), but it also has a transform api to create categorical bins (finally, a sql-based histogram tool!). Now, if only it had a “transpose” function that didn’t require PL/SQL coding, our lives would be much easier…

(BTW, those looking for a transposer (and who isn’t?) for Oracle might enjoy this article from OraMag. Others can be found on Google.)

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