Abstract: A computer-implemented method of creating a data mining model in a database management system comprises accepting a database language statement at the database management system, the database language statement indicating a dataset and a data mining model to be created from the dataset, and creating, in the database management system, the indicated data mining model using the indicated dataset, wherein creation and application of the data mining model does not require moving data to a separate data mining engine.

Abstract: A computer-implemented method of creating a data mining model in a database management system comprises accepting a database language statement at the database management system, the database language statement indicating a dataset and a data mining model to be created from the dataset, and creating, in the database management system, the indicated data mining model using the indicated dataset, wherein creation and application of the data mining model does not require moving data to a separate data mining engine.

Abstract: Decision trees are efficiently represented in a relational database. A computer-implemented method of representing a decision tree model in relational form comprises providing a directed acyclic graph comprising a plurality of nodes and a plurality of links, each link connecting a plurality of nodes, encoding a tree structure by including in each node a parent-child relationship of the node with other nodes, encoding in each node information relating to a split represented by the node, the split information including a splitting predictor and a split value, and encoding in each node a target histogram.

Abstract: A system, software module, and computer program product for performing association rule based data mining that improved performance in model building, good integration with the various databases throughout the enterprise, flexible specification and adjustment of the models being built, and flexible model arrangement and export capability. The software module for performing association rule based data mining in an electronic data processing system comprises: a model setup block operable to receive client input including information specifying a setup of a association rule data mining models, generate the model setup, generate parameters for the model setup based on the received information, a modeling algorithms block operable to select and initialize a association rule modeling algorithm based on the generated model setup, and a model building block operable to receive training data and build a association rule model using the training data and the selected association rule modeling algorithm.

Abstract: A system, software module, and computer program product for performing association rule based data mining that improved performance in model building, good integration with the various databases throughout the enterprise, flexible specification and adjustment of the models being built, and flexible model arrangement and export capability. The software module performs association rule based data mining in an electronic data processing system. A model setup block operable to receive client input including information specifying a setup of a association rule data mining models, generate the model setup, generate parameters for the model setup based on the received information. A modeling algorithms block operable to select and initialize a association rule modeling algorithm based on the generated model setup, and a model building block operable to receive training data and build a association rule model using the training data and the selected association rule modeling algorithm.

Abstract: Techniques are provided for (1) extending SQL to support direct invocation of frequent itemset operations, (2) improving the performance of frequent itemset operations by clustering itemset combinations to more efficiently use previously produced results, and (3) making on-the-fly selection of the occurrence counting technique to use during each phase of a multiple phase frequent itemset operation. When directly invoked in an SQL statement, a frequent itemset operation may receive input from results of operations specified in the SQL statement, and provide its results directly to other operations specified in the SQL statement. By clustering itemset combinations, resources may be used more efficiently by retaining intermediate information as long as it is useful, and then discarding it to free up volatile memory.

Abstract: A method, system, and computer program product for counting predictor-target pairs for a decision tree model provides the capability to generate count tables that is quicker and more efficient than previous techniques. A method of counting predictor-target pairs for a decision tree model, the decision tree model based on data stored in a database, the data comprising a plurality of rows of data, at least one predictor and at least one target, comprises generating a bitmap for each split node of data stored in a database system by intersecting a parent node bitmap and a bitmap of a predictor that satisfies a condition of the node, intersecting each split node bitmap with each predictor bitmap and with each target bitmap to form intersected bitmaps, and counting bits of each intersected bitmap to generate a count of predictor-target pairs.

Abstract: A method, system, and computer program product for counting predictor-target pairs for a decision tree model provides the capability to generate count tables that is quicker and more efficient than previous techniques. A method of counting predictor-target pairs for a decision tree model, the decision tree model based on data stored in a database, the data comprising a plurality of rows of data, at least one predictor and at least one target, comprises generating a bitmap for each split node of data stored in a database system by intersecting a parent node bitmap and a bitmap of a predictor that satisfies a condition of the node, intersecting each split node bitmap with each predictor bitmap and with each target bitmap to form intersected bitmaps, and counting bits of each intersected bitmap to generate a count of predictor-target pairs.

Abstract: Decision trees are efficiently represented in a relational database. A computer-implemented method of representing a decision tree model in relational form comprises providing a directed acyclic graph comprising a plurality of nodes and a plurality of links, each link connecting a plurality of nodes, encoding a tree structure by including in each node a parent-child relationship of the node with other nodes, encoding in each node information relating to a split represented by the node, the split information including a splitting predictor and a split value, and encoding in each node a target histogram.

Abstract: The present invention provides an efficient method and system of data mining using SQL queries for model building and scoring. The invention provides a database management system having a database containing data, a database engine operatively connected to process the data, a SQL server operatively connected to the database and a data mining tool, whereby the data mining tool is based on a Naïve Bayes model. The SQL server uses the data and the Naïve Bayes model to develop the data mining tool. The data mining tool is located is located in the database management system. The data mining tool has a model building system based on at least one SQL query and training data, and a scoring system based on SQL queries.

Abstract: An integrated solution in which BLAST functionality is integrated into a DBMS provides improved performance and scalability over the conventional approach, in addition to reducing the required hardware resources and reducing the cost of the system. In a database management system, a system for sequence matching and alignment comprises a database table storing sequence information comprising target sequences, a set of query sequences, and a table function operable to match the set of query sequences with target sequences stored in the database table, the table function having an interface including parameters.

Abstract: An integrated solution in which BLAST functionality is integrated into a DBMS provides improved performance and scalability over the conventional approach, in addition to reducing the required hardware resources and reducing the cost of the system. In a database management system, a system for sequence matching and alignment comprises a database table storing sequence information comprising target sequences, a query sequence, a table function operable to accept the query sequence and match the query sequence with at least one target sequence stored in the database table, and a structured query language query referencing a database table storing sequence information comprising target sequences, a query sequence, and a table function, the structured query language query evaluatable by the database management system.

Abstract: Techniques are provided for (1) extending SQL to support direct invocation of frequent itemset operations, (2) improving the performance of frequent itemset operations by clustering itemset combinations to more efficiently use previously produced results, and (3) making on-the-fly selection of the occurrence counting technique to use during each phase of a multiple phase frequent itemset operation. When directly invoked in an SQL statement, a frequent itemset operation may receive input from results of operations specified in the SQL statement, and provide its results directly to other operations specified in the SQL statement. By clustering itemset combinations, resources may be used more efficiently by retaining intermediate information as long as it is useful, and then discarding it to free up volatile memory.

Abstract: A method and apparatus for mining data relationships from an integrated database and data-mining system are disclosed. A set of frequent 1-itemsets is generated using a group-by query on data transactions. From these frequent 1-itemsets and the transactions, frequent 2-itemsets are determined. A candidate set of (n+2)-itemsets are generated from the frequent 2-itemsets, where n=1. Frequent (n+2)-itemsets are determined from candidate set and the transaction table using a query operation. The candidate set and frequent (n+2)-itemset are generated for (n+1) until the candidate set is empty. Rules are then extracted from the union of the determined frequent itemsets.