International Business Machines Corporation

Olena Woolf

Martin Oberhofer

Scott Schumacher

Jonathan Limburn

Roger C Raphael

Rajesh M Desai

Michael Cobbett

Arron La

Joseph Kin Wing Tsang

Zahid Naeem

Cross-matching of match indexes assigned to respective data sources can be performed. Each match index can store at least a portion of data from a subset of data records in the data source to which the match index is assigned. Such data can be stored in the match index as match index records. Responsive to identifying matches between match records of different match indexes, the data records in the data sources to which the match records correspond can be updated to add links between the respective data records.

Data resolution among disparate data sources

A data protection policy enforcement operation is provided for enforcing data protection policies in collaborative framework environments which permit a plurality of collaborators to jointly work on projects requiring access to project data assets. For this purpose, a method includes establishing, by a computer device, a plurality of rules for evaluating actions performed in a collaborative environment, the collaborative environment including a plurality of collaborators and a plurality of data assets associated with collaboration between the collaborators; in response to a request to perform an action in the collaborative environment, applying the rules to the plurality of data assets related to the data assets to create a plurality of determinations; in response to each of the plurality of determinations being allowed, allowing the action to be performed; and, in response to at least one of the plurality of determinations being denied, preventing the action from being performed.

Data protection policy enforcement decision making in collaborative framework environments

An ontology is automatically generated for a set of data from multiple data sources. A semantic network of known concepts serves as an ontology template for a target domain of knowledge with known concepts defined as base entity types. Logical groupings of data and associated technical metadata are read from the data sources. Data discovery techniques are applied to detect semantic and/or syntactic classification of data attributes in the logical groupings of data. For each of the logical groupings of data, an entity type with properties for the data attributes are generated and the generated entity type is added to the semantic network with classifications of the properties as derived from the applied data discovery techniques. Semantic meanings of the generated entity types are generated and associated with other entity types within the semantic network and the semantic network is output as a resultant ontology for the set of data.

Automatic ontology generation

A method of processing a plurality of instances in a federated deployment of SOA applications can include the steps of receiving a transaction request to retrieve data, the request specifying retrieving the data stored according to range of indices of the federated deployment, processing each of the instances in a predetermined order to retrieve the data; and calculating a total number of records in the federated deployment.

Growing community of inventors

Toronto, Canada

Olena Woolf