Re: DM: RE: Data Forms for Mining

• Organization: Oracle Corproation

Greg,

Oracle Darwin has no limits on the number of columns or records. We recently did an analysis that contained 7,000 input parameters. It dealt with "medical" type data. We've also successfully mined millions and MANY millions of records. You just need a lot of disk space and memory. Darwin takes advantage of parallel computing techniques so it is designed to handle large amounts of data.

greg.della-croce@marchFIRST.com wrote:

 > Eric,
 >       I think you miss read the line.  It is 700 Variables, not
 > characters.   But
 > that does bring up an interesting question.  IF DM uses a 0NF to lay out 
data
 > (all the info for any given instance in one record)  What are the practical
 > limits of the tool, today, for the number of variables in that 
record.    I am
 > currently working with biomed field and that could very easily go into the
 > hundreds of varables per instance.
 >       Anyone have any input on this?
 >
 > Greg
 >
 > Eric Bloedorn <bloedorn@mitre.org> on 05/22/2000 12:17:37 PM
 >
 > Please respond to datamine-l@nautilus-sys.com
 >
 > To:   datamine-l@nautilus-sys.com
 > cc:    (bcc: Greg Della-Croce/Whittman-Hart LP)
 > Subject:  Re: DM: RE: Data Forms for Mining
 >
 > Ken: I am curious - what commercial tools choke and die on tables wider
 > than 700 chars?!
 >
 > -Eric Bloedorn,
 > MITRE Corporation
 >
 > "Collier, Ken" wrote:
 >   >
 >   > This question hit a hot-button for me. While most OLAP and DSS 
technology
 >   > require a great deal of structure in thier data (e.g., start 
schema), data
 >   > mining tools expect the data to be denormalized into a single 2D table.
 >   > Furthermore, aside from market basket analysis, most data mining 
algorithms
 >   > assume that each observation in a data set represents a unique entity
 > (e.g.,
 >   > each record is a different customer).
 >   >
 >   > What this implies is that there is substantial data preprocessing 
required
 >   > in most cases to transform data from a relational, star, or other
 > structured
 >   > model, into the mineable denormalized structure required. In our 
experience
 >   > with retailers, telecos, manufacturers, insurance companies, banks, and
 >   > others, this preprocessing generally consumes about 80% of the total 
effort
 >   > compared to the actual data mining, validation, verification, and
 >   > deployment, which consumes the remaining 20%. Your mileage may vary.
 >   >
 >   > Now, here's the rub: We recently had a manufacturing client with ~1000
 >   > quality control parameters for each component within a single widget. In
 >   > this scenario a widget is made up of 2-6 major sub-widgets, and each
 >   > sub-widget is made up of 3 components. The same set of QC parameters is
 >   > collected on each component. So, even when we denormalize the data 
into a
 >   > single table, there can be as many as 18 (6 x 3) records for a single
 >   > widget. Our objective in this analysis was to identify root causes of
 > widget
 >   > failure in order to reduce the defect rate.
 >   >
 >   > Now, we want the data mining algorithms to "see" all 18 records 
associated
 >   > w/ a single widget as a single "pattern". Unfortunately commercial tools
 >   > don't tune their algorithms to do this even though it is technically
 >   > possible. One exception is time series and sequence analysis
 > algorithms, but
 >   > these methods are really intended for a different purpose. Another 
kludgy
 >   > solution to this problem is to string out all 18 records into a 
single WIDE
 >   > record per widget. Many commercial tools choke and die on tables 
that are
 >   > wider than 700 vars.
 >   >
 >   > We finally wound up solving this problem using SAS Enterprise Miner 
and SGI
 >   > Mineset, but not without a lot of data transformations, 
preprocessing, and
 >   > preliminary variable reduction. To my thinking, the next generation 
of data
 >   > mining tools should provide the flexibility to "see" data in a wide 
variety
 >   > of structures. The price we may pay for this flexibility is the speed of
 >   > data sourcing prior to analysis.
 >   > ---
 >   > Ken Collier
 >   > Senior Manager, Business Intelligence
 >   > KPMG Consulting
 >   > Corporate Sponsor of the Center for Data Insight 
http://insight.cse.nau.edu
 >   >
 >   > -----Original Message-----
 >   > From: greg.della-croce@marchfirst.com
 >   > [mailto:greg.della-croce@marchfirst.com]
 >   > Sent: Thursday, May 18, 2000 6:09 AM
 >   > To: datamine-l@nautilus-sys.com
 >   > Subject: DM: Data Forms for Mining
 >   >
 >   > I have worked in and around Data Warehouse/Marts with their star 
schema for
 >   > awhile now.   However I am interested what form the data takes when 
it is
 >   > being
 >   > optimized for Mining.   I am speaking to structured data, not 
unstructured
 >   > data
 >   > such as large bodies of text.   What are the architectures of a Data 
Mining
 >   > DB?
 >   > Is the form dependent on the algorithms that you are going to employ
 >   > against it?
 >   > Or is it more general in nature?
 >   >
 >   > Thank you for your replies!
 >   >
 >   > Greg Della-Croce
 >   > marchFirst
 >   > BI/KM
 >   >
 >   >
 > 
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*****************************************************************************

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Charles Berger

Director of Marketing, Data Mining
Oracle Corporation
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