Abstract: A data importer for a sales prospecting system imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.

Abstract: Systems and methods provide a system for generating a sales prospect recommendation that uses demographic data to make a sales prospect recommendation that includes a product recommendation with a probability that the sale will close, and may include an estimated time to close the sale and projected revenue. The system imports customer data including past purchasing data and demographic data for a plurality of customers. The system can then generate a predictive model by training the model using the past purchasing data and the demographic data. When queried for a sales prospect recommendation, the system responds to the query with at least one sales prospect recommended by the predictive model.

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: Techniques are provided for (2) extending SQL to support direct invocation of frequent itemset operations, (3) improving the performance of frequent itemset operations by clustering itemset combinations to more efficiently use previously produced results, and (4) 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: Techniques are provided for (2) extending SQL to support direct invocation of frequent itemset operations, (3) improving the performance of frequent itemset operations by clustering itemset combinations to more efficiently use previously produced results, and (4) 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: 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: 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 system generates a customer reference recommendation based on similarity to other customers. The system includes a customer data file including demographic data and purchasing pattern data for a plurality of customers, a first cluster model trained on the demographic data, and a second cluster model trained on the purchasing pattern data. A customer reference generator produces a customer reference recommendation based on cluster membership in the first and second cluster models in response to a query from a user interface.

Abstract: A data importer for a sales prospecting system imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.

Abstract: Systems and methods provide a system for generating a sales prospect recommendation that uses demographic data to make a sales prospect recommendation that includes a product recommendation with a probability that the sale will close, and may include an estimated time to close the sale and projected revenue. The system imports customer data including past purchasing data and demographic data for a plurality of customers. The system can then generate a predictive model by training the model using the past purchasing data and the demographic data. When queried for a sales prospect recommendation, the system responds to the query with at least one sales prospect recommended by the predictive model.

Abstract: A method and apparatus for performing recursive database operations is provided. According to one aspect, a plurality of first-stage slaves and a plurality of second-stage slaves are established in a database server. During one or more iterations of a recursive database operation, the first-stage slaves concurrently process data items stored in a data repository and send results to the second-stage slaves. The second-stage slaves receive the results and concurrently process those results. The second-stage slaves store the results of the second-stage slaves' processing in the data repository. Subsequent iterations of the recursive database operation proceed in this manner until the recursive database operation has been completed. In each iteration, the first-stage slaves consume the product of the second-stage slaves' previous iteration's processing, and the second-stage slaves consume the product of the first-stage slaves' current iteration's processing.

Abstract: A method and system of collecting execution statistics of query statements is disclosed. An execution plan is generated for a query statement in one embodiment of the invention. The execution plan includes one or more operations. One of the one or more operations is selected. The selected execution plan is then executed and a plurality of execution statistics of the selected operation is collected.

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: Binning of predictor values used for generating a data mining model provides useful reduction in memory footprint and computation during the computationally dominant decision tree build phase, but reduces the information loss of the model and reduces the introduction of false information artifacts. A method of binning data in a database for data mining modeling in a database system, the data stored in a database table in the database system, the data mining modeling having selected at least one predictor and one target for the data, the data including a plurality of values of the predictor and a plurality of values of the target, the method comprises constructing a binary tree for the predictor that splits the values of the predictor into a plurality of portions, pruning the binary tree, and defining as bins of the predictor leaves of the tree that remain after pruning, each leaf of the tree representing a portion of the values of the predictor.

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 method and apparatus for performing recursive database operations is provided. According to one aspect, a plurality of first-stage slaves and a plurality of second-stage slaves are established in a database server. During one or more iterations of a recursive database operation, the first-stage slaves concurrently process data items stored in a data repository and send results to the second-stage slaves. The second-stage slaves receive the results and concurrently process those results. The second-stage slaves store the results of the second-stage slaves' processing in the data repository. Subsequent iterations of the recursive database operation proceed in this manner until the recursive database operation has been completed. In each iteration, the first-stage slaves consume the product of the second-stage slaves' previous iteration's processing, and the second-stage slaves consume the product of the first-stage slaves' current iteration's processing.

Abstract: A method and apparatus for performing recursive database operations is provided. According to one aspect, a plurality of first-stage slaves and a plurality of second-stage slaves are established in a database server. During one or more iterations of a recursive database operation, the first-stage slaves concurrently process data items stored in a data repository and send results to the second-stage slaves. The second-stage slaves receive the results and concurrently process those results. The second-stage slaves store the results of the second-stage slaves' processing in the data repository. Subsequent iterations of the recursive database operation proceed in this manner until the recursive database operation has been completed. In each iteration, the first-stage slaves consume the product of the second-stage slaves' previous iteration's processing, and the second-stage slaves consume the product of the first-stage slaves' current iteration's processing.

Abstract: A system and computer program product provides data mining model deployment (scoring) functionality as a family of SQL functions (operators). A database management system comprises a processor operable to execute computer program instructions, a memory operable to store computer program instructions executable by the processor, and computer program instructions stored in the memory and executable to implement a plurality of database query language statements, each statement operable to cause a data mining function to be performed.

Abstract: A method and apparatus for performing recursive database operations is provided. According to one aspect, a plurality of first-stage slaves and a plurality of second-stage slaves are established in a database server. During one or more iterations of a recursive database operation, the first-stage slaves concurrently process data items stored in a data repository and send results to the second-stage slaves. The second-stage slaves receive the results and concurrently process those results. The second-stage slaves store the results of the second-stage slaves' processing in the data repository. Subsequent iterations of the recursive database operation proceed in this manner until the recursive database operation has been completed. In each iteration, the first-stage slaves consume the product of the second-stage slaves' previous iteration's processing, and the second-stage slaves consume the product of the first-stage slaves' current iteration's processing.

Abstract: A method and mechanism for performing improved frequent itemset operations is provided. A set of item groups are divided into a plurality of subsets. Each item group is composed of a set of data items. Possible combinations of data items that may frequently appear together in the same item group are referred to as candidate combinations. Candidate combinations comprising a first set of data items are identified, and thereafter the occurrence of each candidate combination in any item group in each subset is counted by comparing item bitmaps, associated with items in the candidate combination, in each subset in turn. The comparison of item bitmaps is performed in volatile memory. A total frequent itemset count that describes the frequency of candidate combinations in items groups across all subsets is obtained. Thereafter, the total frequent itemset count for candidate combinations having a larger number of data items may be determined.