Abstract: Systems and methods for providing estimations for a product for purchase at a plurality of stores. Groups of stores are generated based upon similarity of store demand data. For each group, a distribution is determined with respect to the attribute of the product. The distribution is used to provide estimations with respect to the product to be provided at the stores.

Abstract: Computer-implemented systems and methods provide paginated search results from a multi-table database that stores hierarchically arranged data nodes. A query request is received that identifies one or more constraints and one or more monitoring attributes. Records of the multi-table database are filtered to create a view containing only records which meet the constraints. Monitoring attributes associated with a first n records of the view are accessed to generate an output view, where n is the number of records that may be displayed on a single page, and the output view is displayed on a user-viewable medium.

Abstract: A method for learning signatures of a target class using a sequential covering phased rule-induction. The method balances recall and precision for the target class. A first phase aims for high recall by inducing rules with high support and a reasonable level of accuracy. A second phase improves the precision by learning rules to remove false positives in the collection of the records covered by the first phase rules, while keeping the overall recall at a desirable level. The method constructs a mechanism to assign prediction probability scores to each classification decision. The model includes a set of positive rules that predict presence of the target class, a set of negative rules that predict absence of the target class, and a set of prediction score values corresponding to each pair-wise combination of positive and negative rules. The two-phase method is extensible to a multiphase approach.

Abstract: A method for learning signatures of a target class using a sequential covering phased rule-induction. The method balances recall and precision for the target class. A first phase aims for high recall by inducing rules with high support and a reasonable level of accuracy. A second phase improves the precision by learning rules to remove false positives in the collection of the records covered by the first phase rules, while keeping the overall recall at a desirable level. The method constructs a mechanism to assign prediction probability scores to each classification decision. The model includes a set of positive rules that predict presence of the target class, a set of negative rules that predict absence of the target class, and a set of prediction score values corresponding to each pair-wise combination of positive and negative rules. The two-phase method is extensible to a multiphase approach.