1. A system comprising:
a knowledge management component to acquire, classify and disseminate information of a dataset;
a human-computer interaction component to visualize multiple perspectives of the dataset and to model user interactions with the multiple perspectives; and
an adaptivity component to modify one or more of the multiple perspectives of the dataset based on a user-interaction model.
2. The system of claim 1, wherein the knowledge management component, the human-computer interaction component and the adaptivity component comprise multiple codependent subsystems that are linked together in a mutually integrative feedback loop.
3. The system of claim 2, wherein the adaptivity component comprises:
a directed organization subsystem to support user-specified content organization;
a dynamic organization subsystem to support automatic content adaptation to a given user; and
an evaluation subsystem to assess performance of both users and the system and provide feedback to the system for modification.
4. The system of claim 3, wherein the knowledge management component comprises:
a semantic annotation subsystem to capture semantic meaning of, and contextual relationships between, multiple pieces of the information;
a content acquisition subsystem to automatically retrieve new information for the dataset in accordance with one or more specified criteria; and
a classification subsystem to automatically categorize the new information in the dataset using, at least in part, the semantic annotation subsystem.
5. The system of claim 4, wherein the human-computer interaction component comprises:
one or more visualization subsystems to reorganize and present the information of the dataset, and to facilitate user-defined connections among the information; and
a search subsystem to perform semantic-context based text searches.
6. A computer-implemented method comprising:
receiving a definition of a perspective into a dataset associated with N separate ontologies that each specify relationships among data elements in the dataset, wherein N is greater than two, the N separate ontologies correspond to an N dimensional information space, and the data elements are placed in the N dimensional information space according to semantic similarity among ontology elements;
reducing the N dimensional information space to X dimensions based on the perspective definition, wherein X is greater than one and less than N; and
presenting a visual representation of the dataset in the X dimensional space.
7. The method of claim 6, wherein X is three, and presenting the visual representation comprises presenting nodes, which correspond to at least some of the data elements, in the three dimensional space.
8. The method of claim 7, wherein presenting the nodes in the three dimensional space comprises projecting the three dimensional space into a two dimensional display area with the nodes presented using size and brightness to represent distance from a current view point in the three dimensional space.
9. The method of claim 6, wherein the data elements are placed in the N dimensional information space by a process comprising determining distances among the data elements with respect to a given ontology based on a tree structure of the given ontology and estimates of co-relation between the ontology elements of the given ontology, wherein the estimates are based on a corpus including data elements associated with the ontology elements and documents retrieved from a computer network in accordance with terms retrieved from the data elements associated with the ontology elements.
10. The method of claim 6, wherein reducing the N dimensional information space comprises:
performing co-relation analysis for ontology dimensions corresponding to the N separate ontologies;
performing multi-dimensional scaling for the ontology dimensions in accordance with the perspective and weighted distances associated with the ontology elements of the N separate ontologies; and
finding a solution to a stress function corresponding to the multi-dimensional scaling.
11. The method of claim 10, wherein performing the co-relation analysis comprises organizing the ontology dimensions into clusters according to their Pearson Coefficients.
12. The method of claim 10, wherein performing the co-relation analysis comprises performing a principal component analysis (PCA).
13. The method of claim 6, wherein each of the N separate ontologies includes an element used for data for which the ontology is not applicable.
14. A computer-readable medium encoding a computer program product operable to cause data processing apparatus to perform operations comprising:
receiving a definition of a perspective into a dataset associated with N separate ontologies that each specify relationships among data elements in the dataset, wherein N is greater than two, the N separate ontologies correspond to an N dimensional information space, and the data elements are placed in the N dimensional information space according to semantic similarity among ontology elements;
reducing the N dimensional information space to X dimensions based on the perspective definition, wherein X is greater than one and less than N; and
presenting a visual representation of the dataset in the X dimensional space.
15. The computer-readable medium of claim 14, wherein X is three, and presenting the visual representation comprises presenting nodes, which correspond to at least some of the data elements, in the three dimensional space.
16. The computer-readable medium of claim 14, wherein the data elements are placed in the N dimensional information space by a process comprising determining distances among the data elements with respect to a given ontology based on a tree structure of the given ontology and estimates of co-relation between the ontology elements of the given ontology, wherein the estimates are based on a corpus including data elements associated with the ontology elements and documents retrieved from a computer network in accordance with terms retrieved from the data elements associated with the ontology elements.
17. The computer-readable medium of claim 14, wherein reducing the N dimensional information space comprises:
performing co-relation analysis for ontology dimensions corresponding to the N separate ontologies;
performing multi-dimensional scaling for the ontology dimensions in accordance with the perspective and weighted distances associated with the ontology elements of the N separate ontologies; and
finding a solution to a stress function corresponding to the multi-dimensional scaling.
18. A system comprising:
a user interface device; and
one or more computers operable to interact with the user interface device and to perform operations comprising:
receiving a definition of a perspective into a dataset associated with N separate ontologies that each specify relationships among data elements in the dataset, wherein N is greater than two, the N separate ontologies correspond to an N dimensional information space, and the data elements are placed in the N dimensional information space according to semantic similarity among ontology elements;
reducing the N dimensional information space to X dimensions based on the perspective definition, wherein X is greater than one and less than N; and
presenting a visual representation of the dataset in the X dimensional space.
19. The system of claim 18, wherein the one or more computers comprise a server operable to interact with the user interface device through a data communication network, and the user interface device comprises an access device operable to interact with the server as a client.
20. The system of claim 18, wherein the one or more computers comprises one computer, and the one computer comprises the user interface device.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for generating a configuration for an Availability Management Framework (AMF) comprising:
selecting entity types from an entity types file which can provide a set of service instances by first evaluating entity types associated with an application type in said entity types file;
generating entities associated with the selected entity types; and
distributing at least some of the generated entities on nodes of a cluster.
2. The method of claim 1, wherein said step of selecting further comprises:
determining if a service group type associated with said application type supports a predetermined redundancy model;
if said service group type does support said predetermined redundancy model, then determining if (a) a service unit type associated with said service group type can provide a predetermined service and (b) said service unit type can support an expected load associated with said predetermined service; and
if conditions (a) and (b) are met, then selecting, as said entity types, said service unit type, said service group type and said application type.
3. The method of claim 2, wherein if said step of determining if said service group type associated with said application type supports said predetermined redundancy model returns a negative result, then determining if another service group type associated with said application type in said entity types file supports said predetermined redundancy model.
4. The method of claim 3, wherein if no service group type associated with said application type supports said predetermined redundancy model, then evaluating service group types associated with other application types in said entity types file.
5. The method of claim 4, wherein if no service group type associated with any application type in said entity types file supports said predetermined redundancy model, then:
evaluating an orphan service group type in said entity types file to determine if said orphan service group type supports said predetermined redundancy model;
if said orphan service group type does support said predetermined redundancy model, then determining if (a) a service unit type associated with said orphan service group type can provide said predetermined service and (b) said service unit type associated with said orphan service group type can support said expected load associated with said predetermined service;
identifying an adoptive application type which can support said orphan service group type; and
selecting, as said entity types, said service unit type, said orphan service group type and said adoptive application type.
6. The method of claim 5, wherein if no created application types in said entity types file has said orphan service group type as a member or can be extended to provide said orphan service group type, then creating a new application type as said adoptive application type.
7. The method of claim 5, wherein if no orphan service group type supports said predetermined redundancy model, then evaluating at least one orphan service unit type to determine if (a) said at least one orphan service unit can provide said predetermined service and (b) said at least one orphan service unit type can support said expected load associated with said predetermined service;
identifying an adoptive created application type and an adoptive created service group type which can support said at least one orphan service unit type; and
selecting, as said entity types, said orphan service unit type, said adoptive service group type and said adoptive application type.
8. The method of claim 1, further comprising the step of:
storing said generated configuration as a file in an Information Model Management (IMM) eXternal Markup Language (XML) format in a memory device.
9. The method of claim 7, wherein if no orphan service unit is capable of supporting said predetermined service, then evaluating at least one orphan component type to determine if said at least one orphan component type can support said predetermined service and, if so, generating higher level types using said at least one orphan component type.
10. A computer-readable medium containing instructions stored thereon which, when executed by a computer or a processor, perform the steps of:
selecting entity types from an entity types file which can provide a set of service instances by first evaluating entity types associated with an application type in said entity types file; and
generating entities associated with the selected entity types; and
distributing at least some of the generated entities on nodes of a cluster.
11. The computer-readable medium of claim 10, wherein said step of selecting further comprises:
determining if a service group type associated with said application type supports a predetermined redundancy model;
if said service group type does support said predetermined redundancy model, then determining if (a) a service unit type associated with said service group type can provide a predetermined service and (b) said service unit type can support an expected load associated with said predetermined service; and
if conditions (a) and (b) are met, then selecting, as said entity types, said service unit type, said service group type and said application type.
12. The computer-readable medium of claim 11, wherein if said step of determining if said service group type associated with said application type supports said predetermined redundancy model returns a negative result, then determining if another service group type associated with said application type in said entity types file supports said predetermined redundancy model.
13. The computer-readable medium of claim 12, wherein if no service group type associated with said application type supports said predetermined redundancy model, then evaluating service group types associated with other application types in said entity types file.
14. The computer-readable medium of claim 13, wherein if no service group type associated with any application type in said entity types file supports said predetermined redundancy model, then:
evaluating an orphan service group type in said entity types file to determine if said orphan service group type supports said predetermined redundancy model;
if said orphan service group type does support said predetermined redundancy model, then determining if (a) a service unit type associated with said orphan service group type can provide said predetermined service and (b) said service unit type associated with said orphan service group type can support said expected load associated with said predetermined service;
identifying an adoptive application type which can support said orphan service group type; and
selecting, as said entity types, said orphan service unit type, said orphan service group type and said adoptive application type.
15. The computer-readable medium of claim 14, wherein if no application types in said entity types file have said orphan service group type as a member or can be extended to provide said orphan service group type, then creating a new application type as said adoptive application type.
16. The computer-readable medium of claim 14, wherein if no orphan service group type supports said predetermined redundancy model, then evaluating at least one orphan service unit type to determine if (a) said at least one orphan service unit can provide said predetermined service and (b) said at least one orphan service unit type can support said expected load associated with said predetermined service;
identifying an adoptive created application type and an adoptive created service group type which can support said at least one orphan service unit type; and
selecting, as said entity types, said orphan service unit type, said adoptive service group type and said adoptive application type.
17. The computer-readable medium of claim 16, further comprising the step of:
storing said generated configuration as a file in an Information Model Management (IMM) eXternal Markup Language (XML) format in a memory device.
18. The computer-readable medium of claim 16, wherein if no orphan service unit is capable of supporting said predetermined service, then evaluating at least one orphan component type to determine if said at least one orphan component type can support said predetermined service and, if so, generating higher level types using said at least one orphan component type.
19. A system comprising:
a processor for generating a configuration for an Availability Management Framework (AMF), said processor being configured to perform the functions of:
selecting entity types from an entity types file which can provide a set of service instances by first evaluating entity types associated with an application type in said entity types file; and
generating entities associated with the selected entity types; and
distributing at least some of the generated entities on nodes of a cluster.
20. The system of claim 19, wherein said processor selects said entity types by:
determining if a service group type associated with said application type supports a predetermined redundancy model;
if said service group type does support said predetermined redundancy model, then determining if (a) a service unit type associated with said service group type can provide a predetermined service and (b) said service unit type can support an expected load associated with said predetermined service; and
if conditions (a) and (b) are met, then selecting, as said entity types, said service unit type, said service group type and said application type.
21. The system of claim 20, wherein if said processor returns a negative result in determining if said service group type associated with said application type supports said predetermined redundancy model, then said processor determines if another service group type associated with said application type in said entity types file supports said predetermined redundancy model.
22. The system of claim 21, wherein if no service group type associated with said application type supports said predetermined redundancy model, then said processor evaluates service group types associated with other application types in said entity types file.
23. The system of claim 22, wherein if said processor determines that no service group type associated with any application type in said entity types file supports said predetermined redundancy model, then said processor evaluates an orphan service group type in said entity types file to determine if said orphan service group type supports said predetermined redundancy model, and if said orphan service group type does support said predetermined redundancy model, then said processor determines if (a) a service unit type associated with said orphan service group type can provide said predetermined service and (b) said service unit type associated with said orphan service group type can support said expected load associated with said predetermined service, identifies an adoptive application type which can support said orphan service group type, and selects, as said entity types, said orphan service unit type, said orphan service group type and said adoptive application type.
24. The system of claim 23, wherein if said processor determines that no application types in said entity types file has said orphan service unit group type as a member or can be extended to provide said orphan service unit group type, then creating a new application type as said adoptive application type.
25. The system of claim 24, wherein if no orphan service group type supports said predetermined redundancy model, then said processor evaluates at least one orphan service unit type to determine if (a) said at least one orphan service unit can provide said predetermined service and (b) said at least one orphan service unit type can support said expected load associated with said predetermined service, said processor identifies an adoptive created application type and an adoptive created service group type which can support said at least one orphan service unit type; and selects, as said entity types, said orphan service unit type, said adoptive service group type and said adoptive application type.
26. The system of claim 19, further comprising:
a memory device for storing said generated configuration as a file in an Information Model Management (IMM) eXternal Markup Language (XML) format.
27. The system of claim 25, wherein if no orphan service unit is capable of supporting said predetermined service, then said processor evaluates at least one orphan component type to determine if said at least one orphan component type can support said predetermined service and, if so, generates higher level types using said at least one orphan component type.