Abstract: An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

Abstract: Text is extracted from and information resource such as documents, emails, relational database tables and other digitized information sources. The extracted text is processed using a decomposition function to create. Nodes are a particular data structure that stores elemental units of information. The nodes can convey meaning because they relate a subject term or phrase to an attribute term or phrase. Removed from the node data structure, the node contents are or can become a text fragment which conveys meaning, i.e., a note. The notes generated from each digital resource are associated with the digital resource from which they are captured. The notes are then stored, organized and presented in several ways which facilitate knowledge acquisition and utilization by a user.

Abstract: An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

Abstract: An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

Abstract: Techniques for identifying knowledge use an graphical user interface for inputting one or more terms to be explored for additional knowledge. Then a search is conducted across one or more sources of information to identify resources containing information about or information associated with said terms. The resources are decomposed into elemental units of information and stored in a data structures called nodes. A group of nodes are stored in a node pool and, from the node pool, correlations of nodes are constructed that represent knowledge.

Abstract: Text is extracted from and information resource such as documents, emails, relational database tables and other digitized information sources. The extracted text is processed using a decomposition function to create. Nodes are a particular data structure that stores elemental units of information. The nodes can convey meaning because they relate a subject term or phrase to an attribute term or phrase. Removed from the node data structure, the node contents are or can become a text fragment which conveys meaning, i.e., a note. The notes generated from each digital resource are associated with the digital resource from which they are captured. The notes are then stored, organized and presented in several ways which facilitate knowledge acquisition and utilization by a user.

Abstract: An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

Abstract: Text is extracted from and information resource such as documents, emails, relational database tables and other digitized information sources. The extracted text is processed using a decomposition function to create. Nodes are a particular data structure that stores elemental units of information. The nodes can convey meaning because they relate a subject term or phrase to an attribute term or phrase. Removed from the node data structure, the node contents are or can become a text fragment which conveys meaning, i.e., a note. The notes generated from each digital resource are associated with the digital resource from which they are captured. The notes are then stored, organized and presented in several ways which facilitate knowledge acquisition and utilization by a user.

Abstract: An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

Abstract: An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

Abstract: Techniques for identifying knowledge use an graphical user interface for inputting one or more terms to be explored for additional knowledge. Then a search is conducted across one or more sources of information to identify resources containing information about or information associated with said terms. The resources are decomposed into elemental units of information and stored in a data structures called nodes. A group of nodes are stored in a node pool and, from the node pool, correlations of nodes are constructed that represent knowledge.

Abstract: Techniques for identifying knowledge use a graphical user interface for inputting one or more terms to be explored for additional knowledge. Then a search is conducted across one or more sources of information to identify resources containing information about or information associated with said terms. The resources are decomposed into elemental units of information and stored in a data structures called nodes. A group of nodes are stored in a node pool and, from the node pool, correlations of nodes are constructed that represent knowledge using information about relation types. Information about relations types is determined using a relation classifier.

Abstract: Techniques for identifying knowledge use an graphical user interface for inputting one or more terms to be explored for additional knowledge. Then a search is conducted across one or more sources of information to identify resources containing information about or information associated with said terms. The resources are decomposed into elemental units of information and stored in a data structures called nodes. A group of nodes are stored in a node pool and, from the node pool, correlations of nodes are constructed that represent knowledge.

Abstract: Text is extracted from and information resource such as documents, emails, relational database tables and other digitized information sources. The extracted text is processed using a decomposition function to create. Nodes are a particular data structure that stores elemental units of information. The nodes can convey meaning because they relate a subject term or phrase to an attribute term or phrase. Removed from the node data structure, the node contents are or can become a text fragment which conveys meaning, i.e., a note. The notes generated from each digital resource are associated with the digital resource from which they are captured. The notes are then stored, organized and presented in several ways which facilitate knowledge acquisition and utilization by a user.

Abstract: Text is extracted from and information resource such as documents, emails, relational database tables and other digitized information sources. The extracted text is processed using a decomposition function to create. Nodes are a particular data structure that stores elemental units of information. The nodes can convey meaning because they relate a subject term or phrase to an attribute term or phrase. Removed from the node data structure, the node contents are or can become a text fragment which conveys meaning, i.e., a note. The notes generated from each digital resource are associated with the digital resource from which they are captured. The notes are then stored, organized and presented in several ways which facilitate knowledge acquisition and utilization by a user.

Abstract: An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

Abstract: An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

Abstract: A method for processing textual resources may include decomposing the textual resources into a sequence of textual fragments, and searching the sequence of textual fragments for a match to a relational pattern including first and second tokens, and a word based relational bond therebetween. The searching may include searching each textual fragment of the sequence of textual fragments for a match to the word based relational bond, and when a given textual fragment matches the word based relational bond, determining whether the given textual fragment also matches the first and second tokens. The method may include when the given textual fragment also matches the first and second tokens, generating a node having the first and second tokens and the word based relational bond therebetween, and storing the node in a node pool.

Abstract: Member object(s) of a computer system part(s) in an Enterprise Information System (EIS) are identified (discovered) and topographical relationship(s) are established among them to create digital assets by traversing computer file system(s) to find member object(s). For each member object found, a digital asset identifier thereof is placed in an intermediate representation, such as a graph with nodes and edges. Each digital asset identifier corresponds to a node. The edges represent the topographical relationship. A digital asset is created by placing the member object in a logic/data section of the digital asset, attaching an extended environment data structure to the logic/data section and storing the digital asset in an asset inventory container object. This is repeated for each found member object until the intermediate representation fully describes the computer system part and the asset inventory container object is a complete inventory of the digital assets in the computer system part.

Abstract: Provided herein is a system and computer program product for distributing at least one infrastructure description record (IDR) over at least one tier of a network. The system includes at least one network interface that receives the infrastructure description records (IDRs) and the IDRs being enqueued on at least one incoming, transactional, persistent queue (ITPQ), at least one transactional, persistent store, and at least one transactional process that dequeues the IDR from the ITPQ and accesses the IDR to create an accessed IDR, the accessed IDR being stored in the transactional, persistent store in the system.