Abstract: A method and associated system. Multiple virtual triples for an entity of multiple entities identified within a first data source are generated. Each virtual triple consists of a subject, a predicate, and an object. The subject is the entity. The predicate is a relationship between the entity and other entities identified within the first data source. The object is associated with an attribute of the entity. The subject, the predicate, and the object are each identified within the first data source. A degree of similarity between two entities of the two or more entities is identified by comparing the respective frequency metrics of the two entities. The two entities within the data structure are associated in response to a determination that an identified degree of similarity between the two entities exceeds a first predetermined threshold.

Abstract: Methods and systems may provide for technology to conduct a machine learning analysis of data access statistics with respect to a plurality of separate datasets and determine a time-dependent access pattern based on the machine learning analysis, wherein the time-dependent access pattern includes an expert access trend, a curation access trend and a knowledgebase access trend. The technology may also generate one or more data management recommendations with response to the plurality of separate datasets based on the time-dependent access pattern.

Abstract: A method and associated system. Multiple virtual triples for an entity of multiple entities identified within a first data source are generated. Each virtual triple consists of a subject, a predicate, and an object. The subject is the entity. The predicate is a relationship between the entity and other entities identified within the first data source. The object is associated with an attribute of the entity. The subject, the predicate, and the object are each identified within the first data source. A degree of similarity between two entities of the two or more entities is identified by comparing the respective frequency metrics of the two entities. The two entities within the data structure are associated in response to a determination that an identified degree of similarity between the two entities exceeds a first predetermined threshold.

Abstract: A method and associated system. Entities within a first data source are identified. For each entity identified within the first data source, attributes of the entity identified within the first data source and/or relationships between the entity identified within the first data source and other entities identified within the first data source are identified. The attributes and/or relationships identified within the first data source are associated with a first entity identified within a data structure. For each entity identified within the first data source, a frequency metric characterizing the entity identified within the first data source is generated. The frequency metric is based on a frequency at which each attribute and/or relationship identified within the first data source is associated with the entity identified within the first data source. A degree of similarity between two entities of the entities is identified, by comparing the frequency metrics of the two entities.

Abstract: A method to search for at least one relationship pattern in a plurality of runtime artifacts is provided. The method may include detecting at least one data manipulation statement in the plurality of runtime artifacts. The method may also include extracting at least one relationship clause from the detected at least one data manipulation statement. The method may further include parsing the extracted at least one relationship clause. The method may include generating at least one normalized syntax tree based on the parsed at least one relationship clause. The method may also include performing a classification and a snippet discovery on the generated at least one normalized syntax tree.

Abstract: Methods and systems may provide for technology to conduct a machine learning analysis of data access statistics with respect to a plurality of separate datasets and determine a time-dependent access pattern based on the machine learning analysis, wherein the time-dependent access pattern includes an expert access trend, a curation access trend and a knowledgebase access trend. The technology may also generate one or more data management recommendations with response to the plurality of separate datasets based on the time-dependent access pattern.

Abstract: A method to search for at least one relationship pattern in a plurality of runtime artifacts is provided. The method may include detecting at least one data manipulation statement in the plurality of runtime artifacts. The method may also include extracting at least one relationship clause from the detected at least one data manipulation statement. The method may further include parsing the extracted at least one relationship clause. The method may include generating at least one normalized syntax tree based on the parsed at least one relationship clause. The method may also include performing a classification and a snippet discovery on the generated at least one normalized syntax tree.

Abstract: A method to search for at least one relationship pattern in a plurality of runtime artifacts is provided. The method may include detecting at least one data manipulation statement in the plurality of runtime artifacts. The method may also include extracting at least one relationship clause from the detected at least one data manipulation statement. The method may further include parsing the extracted at least one relationship clause. The method may include generating at least one normalized syntax tree based on the parsed at least one relationship clause. The method may also include performing a classification and a snippet discovery on the generated at least one normalized syntax tree.

Abstract: A method and associated system. Entities within a first data source are identified. For each entity identified within the first data source, attributes of the entity identified within the first data source and/or relationships between the entity identified within the first data source and other entities identified within the first data source are identified. The attributes and/or relationships identified within the first data source are associated with a first entity identified within a data structure. For each entity identified within the first data source, a frequency metric characterizing the entity identified within the first data source is generated. The frequency metric is based on a frequency at which each attribute and/or relationship identified within the first data source is associated with the entity identified within the first data source. A degree of similarity between two entities of the entities is identified, by comparing the frequency metrics of the two entities.

Abstract: A method to search for at least one relationship pattern in a plurality of runtime artifacts is provided. The method may include detecting at least one data manipulation statement in the plurality of runtime artifacts. The method may also include extracting at least one relationship clause from the detected at least one data manipulation statement. The method may further include parsing the extracted at least one relationship clause. The method may include generating at least one normalized syntax tree based on the parsed at least one relationship clause. The method may also include performing a classification and a snippet discovery on the generated at least one normalized syntax tree.

Abstract: A first peer computer system operable in a peer to peer network, comprising: means for accessing a first piece of information, means for creating a first entity (e.g. a “token”), means for associating the first entity with the first piece of information (e.g. attaching the first piece of information to the entity) and wherein the first entity is associated with a topic of the information.

Abstract: A first peer computer system operable in a peer to peer network, comprising: means for accessing a first piece of information, means for creating a first entity (e.g. a “token”), means for associating the first entity with the first piece of information (e.g. attaching the first piece of information to the entity) and wherein the first entity is associated with a topic of the information.

Abstract: Device-type-specific XSLT style sheets (or equivalent) are generated from page layout files or ‘presentation skeleton’ files which define a Web page layout including abstract component references that can be mapped to complex, device-specific or repeated style sheet components and are more easily constructed than XSLT style sheets. The presentation skeleton files use and extend existing mark-up languages. One presentation skeleton file can be used to generate XSLT style sheets for many devices that use the same mark-up language. In addition, presentation skeleton files can be produced directly from storyboard files to demonstrate Web sites statically before constructing them for real. This provides a route from initial Web site design to final implementation for multiple output devices.