R. Timarán Pereira, M. Millán, and F. Machuca (Colombia)
Knowledge discovery in databases, data mining, primitives.
Many approaches to implement data mining systems tightly coupled with a Database Management System (DBMS) have been proposed. Expressing certain data mining operations as a series of SQL queries; extending SQL language with unified operators which support certain pattern discovery tasks: DMQL, M-SQL , MINE RULE ; and, defining SQL generic primitive which facilitate the knowledge discovery process without supporting a particular task: NonStop SQL/MX primitives, Count by Group primitive, FilterPartition, ComputeNodeStatistics and PredictionJoin primitives . A major drawback of the first approach of integration is poor performance, due mainly to the fact that the rather simple SQL operations like join, group and aggregation are not sufficient for efficiently executing data mining tasks. In this paper the last two approaches are combined to support new primitives based on new algebraic operators and to integrate a unified operator to efficiently support the association tasks in a DBMS. Algebraic operators, Associator and Extract are proposed. Associator and Extract are implemented in the SQL SELECT clause as ASSOCIATOR RANGE and EXTRACT IN primitives, unified in a new SQL operator for mining association rules called DESCRIBES ASSOCIATION RULES. Auxiliary algebraic operators useful in the association task are also introduced.
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