Scalable data mining in large databases is one of today's challenges to database technologies. Thus, substantial effort is dedicated to a tight coupling of database and data mining systems leading to database primitives supporting data mining tasks. In order to support a wide range of tasks and to be of general usage these primitives should be rather building blocks than implementations of specific algorithms. In this paper, we describe primitives for building and applying decision tree classifiers. Based on the analysis of available algorithms and previous work in this area we have identified operations which are useful for a number of classification algorithms. We discuss the implementation of these primitives on top of a commercial DBMS and present experimental results demonstrating the performance benefit.

Scalable data mining in large databases is one of today's challenges to database technologies. Thus, substantial effort is dedicated to a tight coupling of database and data mining systems leading to database primitives supporting data mining tasks. In order to support a wide range of tasks and to be of general usage these primitives should be rather building blocks than implementations of specific algorithms. In this paper, we describe primitives for building and applying decision tree classifiers. Based on the analysis of available algorithms and previous work in this area we have identified operations which are useful for a number of classification algorithms. We discuss the implementation of these primitives on top of a commercial DBMS and present experimental results demonstrating the performance benefit.

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Abstract.Integrating, cleaning and analyzing data from heterogeneous sources is often complicated by the large amounts of data and its physical distribution which can result in poor query response time. One approach to speed up the processing is to reduce the cardinality of results -either by querying only the first tuples or by obtaining a sample for further processing. In this paper we address the processing of such queries in a multidatabase environment. We discuss implementations of the query operators, strategies for their placement in a query plan and particularly the usage of histograms for estimating attribute value distributions and result cardinalities in order to parameterize the operators.

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