Abstract: A schema modeler for generating an efficient database schema. Provides intelligent choices for schema structure, generates efficient schemas while minimizing the amount of experience required by a database designer. Architectural elements of a schema design are proposed based on field inputs such as field type or relationship. Schema information is manually entered or imported. Configured for structural compatibility and semantic compatibility checking on fields and relationships for data integrity due to nested structure denormalization, inspection of lookup tables that can hold an unlimited number of records, inspection of taxonomy defined on a non-main table, and inspection of the schema for the existence of a main table. Provide suggested field types or schema structures that allow for a more efficient schema to be generated. Field types may include qualifier, multi-lingual, calculation and may include family or attribute table suggestions as well.

Abstract: Adaptive matching of similar data in a data repository to determine if two or more data items are related in accordance with configurable criteria. Matches are adapted by learning and presenting appropriate match criteria based on previous user input. The system can merge the data items into one master data item, group similar items and perform further processing based on the result. The configurable match criteria presented to a user are adapted by the system based on previous interactions of the system with users. Matching is performed by selecting data items to match, removing frequently used strings, normalizing data, tokenizing multi-word data items, assigning weights to each token, calculating a score using the assigned weights, generating groups of similar records, assigning thresholds for match levels. Adapting choices of match criteria for a user based on past interaction allows for rapid match creation and match maintenance that optimizes data integrity across an enterprise.

Abstract: A schema modeler for generating an efficient database schema. Provides intelligent choices for schema structure, generates efficient schemas while minimizing the amount of experience required by a database designer. Architectural elements of a schema design are proposed based on field inputs such as field type or relationship. Schema information is manually entered or imported. Configured for structural compatibility and semantic compatibility checking on fields and relationships for data integrity due to nested structure denormalization, inspection of lookup tables that can hold an unlimited number of records, inspection of taxonomy defined on a non-main table, and inspection of the schema for the existence of a main table. Provide suggested field types or schema structures that allow for a more efficient schema to be generated. Field types may include qualifier, multi-lingual, calculation and may include family or attribute table suggestions as well.

Abstract: Enables locating and merging potential data record matches. Enables locating duplicates through the definition of matching strategies. Each strategy may include transformations, matching rules and scoring thresholds to implement fuzzy matches that match closely related data records that are not exact matches. Performing a search allows for narrowing down the records to a desired set on which to apply a given matching strategy. Merging is performed on the potential duplicates thus located to consolidate data and remove duplicates. By narrowing down the records to a closely related set via a search, merging is simplified to work on a small number of closely related records, thus simplifying the process. The total matching score obtained via execution of match strategies may be utilized to determine which potential record(s) to merge for example. May utilize past user input to provide intelligent inputs for rules, tokens, weights, fields, parameters or any other past user input.

Abstract: Adaptive matching of similar data in a data repository to determine if two or more data items are related in accordance with configurable criteria. Matches are adapted by learning and presenting appropriate match criteria based on previous user input. The system can merge the data items into one master data item, group similar items and perform further processing based on the result. The configurable match criteria presented to a user are adapted by the system based on previous interactions of the system with users. Matching is performed by selecting data items to match, removing frequently used strings, normalizing data, tokenizing multi-word data items, assigning weights to each token, calculating a score using the assigned weights, generating groups of similar records, assigning thresholds for match levels. Adapting choices of match criteria for a user based on past interaction allows for rapid match creation and match maintenance that optimizes data integrity across an enterprise.