Abstract: Persistence and linking of analytic products is provided. Information regarding a plurality of analytic methods is collected. A first process node is generated in a network. The first process node corresponds to a first analytic method. Information is collected regarding a plurality of executions of the first analytic method. A plurality of session nodes is generated in the network corresponding to the plurality of executions. Each of the plurality of session nodes is linked to the first process node. Metadata regarding the plurality of executions is associated with the plurality of session nodes. At least one product node is generated corresponding to a product. The product integrates a result value of at least one of the plurality of executions. The at least one product node is linked to the session node of the plurality of session nodes corresponding to the at least one of the plurality of executions.

Abstract: Persistence and linking of analytic products is provided. Information regarding a plurality of analytic methods is collected. A first process node is generated in a network. The first process node corresponds to a first analytic method. Information is collected regarding a plurality of executions of the first analytic method. A plurality of session nodes is generated in the network corresponding to the plurality of executions. Each of the plurality of session nodes is linked to the first process node. Metadata regarding the plurality of executions is associated with the plurality of session nodes. At least one product node is generated corresponding to a product. The product integrates a result value of at least one of the plurality of executions. The at least one product node is linked to the session node of the plurality of session nodes corresponding to the at least one of the plurality of executions.

Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Persistence and linking of analytic products is provided. Information regarding a plurality of analytic methods is collected. A first process node is generated in a network. The first process node corresponds to a first analytic method. Information is collected regarding a plurality of executions of the first analytic method. A plurality of session nodes is generated in the network corresponding to the plurality of executions. Each of the plurality of session nodes is linked to the first process node. Metadata regarding the plurality of executions is associated with the plurality of session nodes. At least one product node is generated corresponding to a product. The product integrates a result value of at least one of the plurality of executions. The at least one product node is linked to the session node of the plurality of session nodes corresponding to the at least one of the plurality of executions.

Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Embodiments of a method for creating a graph database which is arranged to store data in the form of graph tuples comprising n-parts, are described. In an embodiment, each tuple-part is encoded into a unique part identifier (UPI). A geospatial UPI is defined for two-dimensional data elements that include a plurality of data points defined by respective pairs of an X-ordinate and a Y-ordinate. A two-dimensional data space is divided into a plurality of uniform Y strips of defined widths. The Y-strip width is selected to correspond approximately to an anticipated Y dimension of a typical search region of interest. The geospatial UPI encodes a plurality of coordinate points in a two -dimensional data space into a single datum that includes respective pairs of an X-ordinate and a Y-ordinate. The geospatial UPI comprises a data structure arranged in order of sort priority by the geospatial UPI type code, geospatial subtype, and Y-strip identifier followed by the X-ordinate data and Y-ordinate data.

Abstract: Embodiments of a method for creating a graph database which is arranged to store or process data in the form of graph tuples comprising n-parts, are described. In an embodiment, each tuple-part is encoded into a unique part identifier (hereinafter called a UPI), each UPI comprises a tag at a fixed position within the UPI. The tag indicates the datatype of the encoded tuple-part. The content data for the tuple-part is encoded in a code that is configured to reflect the ranking or order of the content data, corresponding to each datatype, relative to other tuples in a set of tuples. For content data that comprises a character-string, the code comprises a hashcode; and for content data that comprises or includes a numeric value, the code comprises an immediate value that directly stores the numeric value without encoding.

Abstract: Embodiments of a method for creating a graph database which is arranged to store or process data in the form of graph tuples comprising n-parts, are described. In an embodiment, each tuple-part is encoded into a unique part identifier (hereinafter called a UPI), each UPI comprises a tag at a fixed position within the UPI. The tag indicates the datatype of the encoded tuple-part. The content data for the tuple-part is encoded in a code that is configured to reflect the ranking or order of the content data, corresponding to each datatype, relative to other tuples in a set of tuples. For content data that comprises a character-string, the code comprises a hashcode; and for content data that comprises or includes a numeric value, the code comprises an immediate value that directly stores the numeric value without encoding.

Abstract: Embodiments of a method for creating a graph database which is arranged to store data in the form of graph tuples comprising n-parts, are described. In an embodiment, each tuple-part is encoded into a unique part identifier (UPI). A geospatial UPI is defined for two-dimensional data elements that include a plurality of data points defined by respective pairs of an X-ordinate and a Y-ordinate. A two-dimensional data space is divided into a plurality of uniform Y strips of defined widths. The Y-strip width is selected to correspond approximately to an anticipated Y dimension of a typical search region of interest. The geospatial UPI encodes a plurality of coordinate points in a two-dimensional data space into a single datum that includes respective pairs of an X-ordinate and a Y-ordinate. The geospatial UPI comprises a data structure arranged in order of sort priority by the geospatial UPI type code, geospatial subtype, and Y-strip identifier followed by the X-ordinate data and Y-ordinate data.

Abstract: A communication system has a plurality of user terminals, operable by a corresponding pourality of users, connecting means, a “personal assistant” server and a “personal assistant” terminal. The “personal assistant” terminal is connected to the “personal assistant” server and operated by a human “personal assistant”. Any of the users, using his(her) user terminal, can contact (call) the human assistant, via the personal assistant terminal, to obtain information and/or other services. The server is connected to a user database which contains user data for all the users, with that data containing user-specific data and settings. The server, on detecting a call from any one of the user terminals to the personal assistant terminal, accesses the user data form the database and associated with the calling user. That data is provided to the personal assistant terminal.

Abstract: Method and customer information system with a processor and memory, from which information is retrievable, whereby such system: can communicate with several remote data sources; can import from those several data sources several data streams, which may have a different data format in each instance; can convert the several data streams to unified data streams with a unified data format; can store the unified data streams in the memory; can import updated data at predetermined times from the associated external data source.

Abstract: A method and a system for speeding up and reducing the latency in retrieving data of a data network (1). The network (1) being of the type from which data can be retrieved as data pages, among others, multi-level data pages from a plurality of data servers (2) and by a plurality of users (6; 16). Users are provided with an editable navigation tool (19) for retrieving data pages from the network (1). Data pages and data page levels retrieved by a user (6; 16) with the aid of the navigation tool (19) are analyzed and stored in a personal proxy (9; 11) associated with the user (6; 16) and featured by a cache memory (10; 12). The cache memory (10; 12) is refreshed by retrieving relevant data pages and data page levels from the data network (1). Data pages and data page levels requested with the aid of the navigation tool (19) are at first instance retrieved from the personal proxy (9, 11) and, if not available, from the data network (1).

Abstract: The invention pertains to a method, a system and an agent for sending information over a communication network. In the vicinity of a server in the communication network, there is an agent functionality that helps serve one or more clients or users by providing relevant information from a cache containing up-to-date information. The agent can be operated by the user and can also learn the usage behavior of the client.

Abstract: Communication system comprising user terminals (2, 12) operable by users (1) and connecting means (3). Connected to a “personal assistant” server (6) is a terminal (7), operated by a human “personal assistant” (8) who can be called up by users to provide information or offer other services. The server (6) is connected to a users database (9) containing user-specific data and settings, whereby the server, on detection of a call from a user terminal (2, 12) to the “personal assistant” terminal (7), reads out the data and settings of the calling user from the database. The server (6) is connected to internal or external data systems (13, 15), which are accessible and operable by the “personal assistant” terminal (7) with control by user data and settings called up from the users database (9). The data systems can be an e-mail system, a diary system, internal or external information systems, an SMS system, a fax system, a unified messaging system, etc.

Abstract: To improve access to information stored in data networks such as the World Wide Web (11), a method of and system for creating a button type bookmark (12, 13) of an addressable data file displayed on a screen (3) of a computer device (1) is provided, comprising the steps of: providing a draggable representation (14) of the button type bookmark (12, 13); positioning (15) this representation (14) at part of the data file (4) displayed on the screen (3); capturing from a screen buffer (8) the part of the data file (4) covered by the representation (14); copying the captured part into a data structure representing the button type bookmark (12, 13); and storing the data structure and address information associated with the data file (4) in a bookmarks buffer (7).

Abstract: System for dissemination of digital documents comprising a user client (1) and a dissemination server (10). The user client comprises I/O means (2) and processing means (3) for processing (viewing, storing, editing etc.) the documents. Logging means (4) register processing events as logging records. Grouping means (5) register document groups or folders in which the relevant documents may be stored. The dissemination server comprises I/O means (11), first document classification means (12) for assigning first classification codes derived form the relevant document's content. Second document classification means (13) receive from the logging means (4) a first subset of the logging records and assign second classification codes. First user profile means (16) receive from the client's grouping means (5) the registered document groups and assign first user interest codes based on those document groups. Second user profile means (17) assign second user interest codes based on a received second subset.