Oracle International Corporation

Ari Wolfe Mozes

Wei Ey Li

Marcos M Campos

Joseph Yarmus

Shiby Thomas

Mahesh Jagannath

Jiansheng Huang

Peter J Stengard

Krisztian Danko

Francisco V Casas

Jooyoung John Kim

Benoit Dageville

Thierry Cruanes

Hakan Jakobsson

Andrew Witkowski

Mohamed Zait

Cetin Ozbutun

Boriana L Milenova

Arvind Nithrakashyap

Todd Guay

Chi Young Ku

Mark A McCracken

Fei Ge

Gregory Scott Smith

Mark D Callaghan

Gaylen D Royal

Chitra Balwant Bhagwat

Dongfang Bai

Gayathri P Ayyappan

Systems, methods, and other embodiments are disclosed for partitioning models in a database. In one embodiment, a set of training data is parsed into multiple data partitions based on partition keys, where the data partitions are identified by the partition keys and are used for training data mining models. The multiple data partitions are analyzed to generate partition metrics data. Algorithm data, identifying at least one algorithm for processing the multiple data partitions, and resources data, identifying available modeling resources for processing the multiple data partitions, are read. The partition metrics data, the algorithm data, and the resources data are processed to generate an organization data structure. The organization data structure is configured to control distribution and processing of the multiple data partitions across the available modeling resources to generate a composite model object that includes a separately trained data mining model for each partition of the multiple partitions.

System and method for partitioning models in a database

Systems, methods, and other embodiments are disclosed for performing data mining. In one embodiment, transaction records are read one at a time. Each transaction record represents a transaction for at least one item and includes an item identifier and a metric value for the item. The number-of-occurrences of at least one candidate item set in the transaction records are counted to generate a total count for the candidate item set. The candidate item set includes one or more items. As the counting proceeds, at least one aggregate metric value associated with the candidate item set is accumulated by summing metric values across the number-of-occurrences for each item represented in the candidate item set. A determination is made as to whether the candidate item set is a frequent item set in the transaction records by comparing the total count to a threshold value.

System and method providing association rule aggregates

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.

Data importer for a sales prospector

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.

System and method for building decision trees in a database

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.

Hybrid prediction model for a sales prospector

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. Dynamically selecting an occurrence counting technique allows a single frequent itemset operation to change the occurrence counting technique that it is using midstream, based on cost considerations and/or environmental conditions.

Expressing frequent itemset counting operations

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. Dynamically selecting an occurrence counting technique allows a single frequent itemset operation to change the occurrence counting technique that it is using midstream, based on cost considerations and/or environmental conditions.

Frequent itemset counting using clustered prefixes and index support

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.

Binning predictors using per-predictor trees and MDL pruning

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.

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.

Frequent itemset counting using subsets of bitmaps

Dynamic selection of frequent itemset counting technique

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.

Performing recursive database operations

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.

Growing community of inventors

Lexington, MA, United States of America

Ari Wolfe Mozes