AI Seminar
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    Tuesday, April 6th, 2004
    4:00 pm - 5:30 pm
    1504 G.G. Brown (Lee Iacocca Room)  **NOTE THE DIFFERENT LOCATION**

    "Data Mining Large Databases: Scalability Considerations"

    Dr. Usama Fayyad
    President, DMX Group




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Data mining methods have grown in importance as data sets grew larger 
and more numerous. Many of the fundamental problems in performing 
data mining tasks rely on statistical estimation and modeling. 
However, many of the computational advances in statistical analysis 
methods paid little attention to the problems of scaling to massive 
data sets (in contrast to much of the work on the database systems 
side where scalability is a central theme). In this talk, we present 
several algorithms and considerations in the area of scaling data 
mining algorithms to large databases. The approaches fundamentally 
rely on the notion of decomposing algorithms into basic components 
that more easily lend themselves to scaling to large data. It turns 
out that most popular algorithms can be decomposed into components 
that need to be close to the data, and others than can operate over 
reduced forms or sufficient statistics of the data. The key to a good 
decomposition is to keep the components that need to "touch the data" 
simple and fast. In addition, it is important to consider the number 
of times an algorithm requires a scan of the data. After covering a 
couple of illustrative examples of scaling algorithms to large 
databases, we consider the converse approach: can we utilize 
fundamental notions in data mining to help solve classical database 
problems such as indexing high-dimensional data and estimating query 
selectivity etc. The theme here is that database considerations are 
important in data mining while statistical and data mining 
considerations play an important role in database systems 
considerations. We wrap-up the discussion of databases with a brief 
coverage of some work on integrating data mining in a major 
commercial database system (Microsoft SQL Server).  We conclude the 
talk with a summary of the numerous remaining technical challenges 
facing the field of data mining.