International Business Machines Corporation

Mohammad Khatibi

Stephanie J Hazlewood

Amira Nancy Taiyab

Lan Luo

Susanna W Tai

Soma Shekar Naganna

Abhishek Seth

Neeraj Ramkrishna Singh

Geetha Sravanthi Pulipaty

Mark Wang

Martin Oberhofer

Scott Schumacher

James Albert O'Neill, Jr

Pushpalatha M Hiremath

Yongli An

Alexandre Luz Xavier Da Costa

Ruolin Wu

Karen Dixon Chouinard

Sachin Wadhwa

An approach for accessing multi-attribute data records of a master data management system. The method comprises: enhancing the master data management system with one or more search engines for enabling data record access. A request of data may be received at the master data management system. A set of one or more of the multiple attributes, referenced in the received request, may be identified. A combination of one or more of the search engines of the master data management system, whose performances for searching values of at least part of the set of attributes fulfil a current selection rule may be selected. And, the request may be processed using the combination of search engines. At least part of the results of the processing may be provided, and the selection rule may be updated based on user operations on the provided results, the updated selection rule becoming the current selection rule.

Method for accessing data records of a master data management system

A method, a computer system, and a computer program product for data validation in a microservice environment is provided. Embodiments of the present invention may include receiving a request based on an application configuration validation. Embodiments of the present invention may include determining a schema is not defined based on the request. Embodiments of the present invention may include generating the schema using machine learning. Embodiments of the present invention may include using the generated schema for a plurality of validations.

Governance based validation framework for JSON data using machine learning

Techniques for data evaluation are provided. A plurality of data records is received, and a first comparison vector is generated by comparing a first and a second data record of the plurality of data records, where the first comparison vector indicates differences between the first and second data records. A machine learning model is trained based at least in part on the first comparison vector. The plurality of data records is evaluated using the machine learning model, and at least two of the plurality of data records are linked based on the evaluation.

Machine learning to tune probabilistic matching in entity resolution systems

A method is disclosed for a database system that includes a set of data blocks comprising records having attributes. The set of data blocks are instances of at least one block type. The block type is defined by a subset of one or more attributes of the attributes. An instance of the block type comprises records having one distinct group of values of the subset of attributes. The method includes detecting that a subset of one or more data blocks of the block type, of the set of data blocks, reached a first maximum number of records. The method includes determining an additional attribute of the attributes to define a new block type by the combination of the additional attribute and the subset of attributes that define the block type. The method includes creating one or more data blocks which are instances of the new block type.

Method for dynamic data blocking in a database system

Systematically enforcing generation of partition key values for records in a data management system such that for any given partitioned table, all records relating to a common business object share a common partitioning key. Partitioning and/or clustering of these records can then be done based on these specially generated partitioning key values, with entities dependent on the same first-class entity being placed in the same partition and/or cluster. This may provide considerable performance gains in at least some circumstances.

Data partitioning for high-efficiency data retrieval

According to one embodiment of the present invention, a system for managing data within a plurality of data management architectures includes at least one processor. The system persists an entity managed by a first data management architecture to a second data management architecture. The first data management architecture manages entity data within data sources and the second data management architecture manages persisted entities within a common repository. Entity attributes are mapped between the first and second data management architectures. The system further provides one or more supplemental attributes for the persisted (e.g., registration mode or fully persisted mode) entity within the second data management architecture, wherein the supplemental attributes are unmapped between the first and second data management architectures. Embodiments of the present invention further include a method and computer program product for managing data within a plurality of data management architectures in substantially the same manner described above.

Plural architecture master data management with supplemental attributes

According to one embodiment of the present invention, a system for managing data within a plurality of data management architectures comprises at least one processor. The system persists an entity managed by a first data management architecture to a second data management architecture. The first data management architecture manages entity data within one or more data sources, while the second data management architecture manages persisted entities with data from the one or more data sources within a common repository. A modification to data of the persisted entity is detected within the one or more data sources, and the modified data is synchronized with the persisted entity in the repository of the second data management architecture. Embodiments of the present invention further include a method and computer program product for managing data within a plurality of data management architectures in substantially the same manner described above.

Plural architecture master data management

Provided are techniques for dynamic assignment of business logic based on schema mapping metadata. A first transformation is performed using a simplified map and a structural transformation map that maps attributes between a source system and a target system to generate simplified map data that includes source attribute and target attribute pairs. A document that includes combined data is generated using the simplified map data and source system metadata containing contextual values of the source system. A second transformation is performed to generate transformed contextual metadata containing contextual values for the target system using a contextual metadata transformation map and the combined data.

Dynamic assignment of business logic based on schema mapping metadata

The present invention in various implementations provides for a method and system for removing suspect duplicate data in a database having a plurality of datasets for a suspect processing transaction, using a plurality of matching engines, comparing results of matching engines in a logically predetermined comparative assessment, and thereafter providing an ordered priority of results of matching engines to identify suspect candidates.

Growing community of inventors

Richmond Hill, Canada

Mohammad Khatibi