1. A method for cluster management in a network environment, comprising:
receiving one or more traffic measurements from one or more nodes associated with one or more clusters in said network environment;
determining, in accordance with the received traffic measurements, one or more reclustering operations to be performed in said network environment; and
dispatching data to realize said reclustering.
2. The method of claim 1, wherein said network environment is a peer-to-peer environment.
3. The method of claim 1, wherein said reclustering operations comprise creation of a new cluster.
4. The method of claim 1, wherein said reclustering operations comprise elimination of one of said clusters.
5. The method of claim 1, wherein said reclustering operations comprise transfer of one or more of said nodes among between one or more of said clusters.
6. The method of claim 1, wherein said traffic measurements are constantly taken.
7. The method of claim 1, wherein said traffic measurements are taken in response to a request for said measurements.
8. The method of claim 7, wherein said measurements are taken for a specified period of time.
9. The method of claim 1, wherein said traffic measurements comprise measurements corresponding to node index updates.
10. The method of claim 1, wherein said traffic measurements comprise measurements corresponding to entity index updates.
11. The method of claim 1, wherein said traffic measurements comprise measurements corresponding to entity queries.
12. The method of claim 1, wherein determining comprises determination of ideal cluster size.
13. The method of claim 1, wherein a new cluster is created in response to one or more of said traffic measurements rising above a specified level.
14. The method of claim 1, wherein one of said clusters is eliminated in response to one or more of said traffic measurements falling below a specified level.
15. A method for cluster management in a network environment, comprising:
receiving a request from a node to change affiliation with said network environment;
determining if the affiliation change would result in an integer-squared number of nodes being affiliated with said environment; and
dispatching data to realize reclustering in said environment in the case where said determining yields an affirmative result.
16. The method of claim 15, wherein said network environment is a peer-to-peer environment.
17. The method of claim 15, wherein it is determined if the affiliation change would result in said integer-squared number of nodes being registered in said network environment.
18. The method of claim 15, wherein it is determined if the affiliation change would result in said integer-squared number of nodes being active in said network environment.
19. The method of claim 15, wherein the affiliation change is registration.
20. The method of claim 15, wherein the affiliation change is entry into active state.
21. The method of claim 15, wherein said reclustering comprises establishment of a new cluster in said network environment.
22. The method of claim 15, wherein said reclustering comprises elimination of an existing cluster in said network environment.
23. The method of claim 15, wherein said reclustering comprises transfer of one or more nodes from a first cluster in said network envirornent to a second cluster in said network environment.
24. A method for communications in a network environment, comprising:
receiving data at a node in said network environment, wherein said node is associated with a cluster in said network environment;
selecting from identification numbers associated with nodes in said network environment an identification number closest in value, in view of a specified polarity, to an identification number associated with said node; and
dispatching said data to a node associated with the selected identification number.
25. The method of claim 24, wherein said network environment is a peer-to-peer environment.
26. The method of claim 24, wherein the identification number associated with the node that received said data and the selected identification number are node identification numbers, and said node associated with the selected identification number is associated with said cluster.
27. The method of claim 24, wherein the identification number associated with the node that received said data and the selected identification number are cluster identification numbers, and node associated with the selected identification number is associated with a cluster other than the cluster with which the node that received said data is associated.
28. The method of claim 27, wherein said node associated with the selected identification number is selected randomly from a plurality of nodes associated with the selected identification number.
29. The method of claim 26, wherein said data corresponds to an entity index update.
30. The method of claim 3, wherein said data corresponds to an entity query.
31. The method of claim 26, wherein said data corresponds to a node index update.
32. The method of claim 27, wherein said data corresponds to a node index update.
33. The method of claim 24, wherein said specified polarity indicates that the selected identification number be higher in value than the identification number associated with the node that received said data.
34. The method of claim 24, wherein said specified polarity indicates that the selected identification number be lower in value than the identification number associated with the node that received said data.
35. A system for cluster management in a network environment, comprising:
a memory having program code stored therein; and
a processor operatively connected to said memory for carrying out instructions in accordance with said stored program code;
wherein said program code, when executed by said processor, causes said processor to perform:
receiving one or more traffic measurements from one or more nodes associated with one or more clusters in said network environment;
determining, in accordance with the received traffic measurements, one or more reclustering operations to be performed in said network environment; and
dispatching data to realize said reclustering.
36. The system of claim 35, wherein said network environment is a peer-to-peer environment.
37. The system of claim 35, wherein said reclustering operations comprise creation of a new cluster.
38. The system of claim 35, wherein said reclustering operations comprise elimination of one of said clusters.
39. The system of claim 35, wherein said reclustering operations comprise transfer of one or more of said nodes among between one or more of said clusters.
40. The system of claim 35, wherein said traffic measurements are constantly taken.
41. The system of claim 35, wherein said traffic measurements are taken in response to a request for said measurements.
42. The system of claim 41, wherein said measurements are taken for a specified period of time.
43. The system of claim 35, wherein said traffic measurements comprise measurements corresponding to node index updates.
44. The system of claim 35, wherein said traffic measurements comprise measurements corresponding to entity index updates.
45. The system of claim 35, wherein said traffic measurements comprise measurements corresponding to entity queries.
46. The system of claim 35, wherein determining comprises determination of ideal cluster size.
47. The system of claim 35, wherein a new cluster is created in response to one or more of said traffic measurements rising above a specified level.
48. The system of claim 35, wherein one of said clusters is eliminated in response to one or more of said traffic measurements falling below a specified level.
49. A system for cluster management in a network environment, comprising:
a memory having program code stored therein; and
a processor operatively connected to said memory for carrying out instructions in accordance with said stored program code;
wherein said program code, when executed by said processor, causes said processor to perform:
receiving a request from a node to change affiliation with said network environment;
determining if the affiliation change would result in an integer-squared number of nodes being affiliated with said environment; and
dispatching data to realize reclustering in said environment in the case where said determining yields an affirmative result.
50. The system of claim 49, wherein said network environment is a peer-to-peer environment.
51. The system of claim 49, wherein it is determined if the affiliation change would result in said integer-squared number of nodes being registered in said network environment.
52. The system of claim 49, wherein it is determined if the affiliation change would result in said integer-squared number of nodes being active in said network environment.
53. The system of claim 49, wherein the affiliation change is registration.
54. The system of claim 49, wherein the affiliation change is entry into active state.
55. The system of claim 49, wherein said reclustering comprises establishment of a new cluster in said network environment.
56. The system of claim 49, wherein said reclustering comprises elimination of an existing cluster in said network environment.
57. The system of claim 49, wherein said reclustering comprises transfer of one or more nodes from a first cluster in said network environment to a second cluster in said network environment.
58. A system for communications in a network environment, comprising:
a memory having program code stored therein; and
a processor operatively connected to said memory for carrying out instructions in accordance with said stored program code;
wherein said program code, when executed by said processor, causes said processor to perform:
receiving data at a node in said network environment, wherein said node is associated with a cluster in said network environment;
selecting from identification numbers associated with nodes in said network environment an identification number closest in value, in view of a specified polarity, to an identification number associated with said node; and
dispatching said data to a node associated with the selected identification number.
59. The system of claim 58, wherein said network environment is a peer-to-peer environment.
60. The system of claim 58, wherein the identification number associated with the node that received said data and the selected identification number are node identification numbers, and said node associated with the selected identification number is associated with said cluster.
61. The system of claim 58, wherein the identification number associated with the node that received said data and the selected identification number are cluster identification numbers, and node associated with the selected identification number is associated with a cluster other than the cluster with which the node that received said data is associated.
62. The system of claim 61, wherein said node associated with the selected identification number is selected randomly from a plurality of nodes associated with the selected identification number.
63. The system of claim 60, wherein said data corresponds to an entity index update.
64. The system of claim 37, wherein said data corresponds to an entity query.
65. The system of claim 60, wherein said data corresponds to a node index update.
66. The system of claim 61, wherein said data corresponds to a node index update.
67. The system of claim 58, wherein said specified polarity indicates that the selected identification number be higher in value than the identification number associated with the node that received said data.
68. The system of claim 58, wherein said specified polarity indicates that the selected identification number be lower in value than the identification number associated with the node that received said data.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
What is claimed is:
1. A method to arbitrate between results to effectuate a desired result, said results to be arbitrated obtained by a plurality of procedures from observationally derived data, said method comprising the steps of:
a. ranking each of the procedures according to confidence on each of said procedures;
b. classifying each of the results according to a level of accuracy at which each of said results matches a datum from a database of a plurality of databases;
c. comparing each of the results to each other;
d. generating rules, said rules utilizing as inputs said results and information derived from said results;
e. selecting at least one of said rules;
f. applying said at least one of said rules according to an algorithm;
g. obtaining, from the application said at least one of said rules, said desired result.
2. The method of claim 1 further comprising the step of:
appending an indicator to each result, said indicator indicating an outcome of applying said at least one rule.
3. The method of claim 1 further comprising the steps of:
comparing the results to a plurality of known incorrect results;
discarding any of said results that matches any one of said known incorrect results;
and, wherein steps (a) through (g) are applied only to the said results that are not discarded.
4. The method of claim 1 further comprising the steps of:
obtaining at least one other indicator for each result, said at least one other indicator indicating a degree of success of said comparative step for each observationally derived datum.
5. The method of claim 4 further comprising the steps of:
creating a data structure, said data structure comprising:
said results;
said desired result obtained by applying said at least one of said rules;
one further other indicator indicating said ranking according to confidence for each of said results;
said at least one other indicator for each of the results.
6. The method of claim 1 further comprising the step of:
assigning to each of said results yet another indicator of said ranking of confidence of the procedure that produced said result.
7. The method of claim 1 wherein said observationally derived data comprises data derived from addressed pieces and wherein said result comprises an alphanumerical code.
8. The method of claim 7 further comprising the step of: obtaining at least one indicator for one of said alphanumerical codes, said one of said alphanumerical codes resulting from applying one of said procedures to specific observationally derived data, said at least one indicator indicating the number of alphanumerical codes resulting from applying other ones of said plurality of procedures to said specific observationally derived data that are congruent to said one of said alphanumerical codes for a given number digits of said one of said alphanumerical codes.
9. The method of claim 1 wherein said rules comprise detecting specific information in observationally derived data.
10. The method of claim 7 wherein said rules comprise detecting return or forwarding information in said data derived from addressed pieces.
11. The method of claim 7 wherein said rules comprise detecting a lack of congruency of a group of digits in each one of said alphanumerical codes obtained from specific observationally derived data by several ones of said plurality of procedures.
12. The method of claim 7 wherein said rules comprise detecting congruency of a group of digits in each one of said alphanumerical codes obtained from specific observationally derived data by several ones of said plurality of procedures.
13. The method of claim 7 wherein said rules comprise ignoring, when more than one result is obtained corresponding to one observationally derived datum by applying a plurality of procedures, the results corresponding to said one observationally derived datum and obtained from other ones of said plurality of procedures having a lower ranking.
14. The method of claim 7 wherein said rules comprise a relationship between a time period, said time period comprising the difference between when an addressed piece was sent and when said observationally derived data was obtained, and a probability of inaccuracy of said results obtained from the observationally derived data corresponding to said addressed piece.
15. The method of claim 7 wherein said rules comprise utilizing knowledge of details of each of said plurality of procedures to determine a confidence level to each said comparison of each result corresponding to one observationally derived datum to another result for said one observationally derived datum obtained from another one of said plurality of procedures.
16. A computer program product comprising: a computer usable medium having computer readable code embodied therein for arbitrating between results to effectuate a desired result, said results obtained from observationally derived data by a plurality of procedures, said code causing a computer system to:
(a) rank each of the procedures according to confidence on each said procedure;
(b) classify each of the results according to a level of accuracy at which each of the results matches a datum from a database of a pluralities of databases;
(c) compare each of the results to each other;
(d) select at least one rule, said at least one rule utilizing as inputs said results and information derived from said results;
(e) apply said at least one rule according to an algorithm;
(f) obtain, from applying said selected rules, said desired result.
17. The computer program product of claim 16 where the computer readable code further causes a computer system to:
compare the results to a plurality of known incorrect results;
discard any of said results that matches any one of said known incorrect results;
and, wherein the computer system applies steps (a) through (f) to the non-discarded results.
18. The computer program product of claim 16 where the computer readable code further causes a computer system to:
append an indicator to each result, said indicator indicating an outcome of applying said at least one rule.
19. The computer program product of claim 16 where the computer readable code further causes a computer system to:
obtain at least one other indicator for each result, said at least one other indicator indicating a degree of success of said comparative step.
20. The computer program product of claim 19 where the computer readable code further causes a computer system to:
create a data structure, said data structure comprising:
said results;
said desired result obtained by applying said selected at least one rule;
one further other indicator indicating said ranking according to confidence for each of said results;
said at least one other indicator for each result.
21. The computer program product of claim 16 wherein said observationally derived data comprises data derived from addressed pieces and wherein said result comprises an alphanumerical code.
22. The computer program product of claim 16 where the computer readable code further causes a computer system to:
generate said at least one rule.
23. The computer program product of claim 21 where the computer readable code further causes a computer system to:
obtain at least one indicator for one of said alphanumerical codes, said one of said alphanumerical codes resulting from applying one of said procedures to specific observationally derived data, said at least one indicator indicating the number of alphanumerical codes resulting from applying other ones of said plurality of procedures to said specific observationally derived data that are congruent to said one of said alphanumerical codes for a given number digits of said one of said alphanumerical codes.
24. A system including arbitration between results to effectuate a desired result, said results obtained by a plurality of procedures from observationally derived data, the result being items characteristic of the observationally derived data, said system comprising:
at least one processor;
at least one computer readable memory having instructions embodied therein, said instructions causing said at least one processor to:
rank each of the procedures according to confidence on each said procedure;
classify each of the results according to a level of accuracy at which it matches a datum from a database of a plurality of databases;
compare each of the results to each other;
select at least one rule, said at least one rule utilizing as inputs said results and characteristics derived from said results;
apply said at least one rule according to an algorithm;
obtain, from applying said at least one rule, said desired result.
25. The system of claim 24 wherein the instructions embodied in the at least one computer readable memory further cause said at least one processor to:
append an indicator to each result, said indicator indicating an outcome of applying said at least one rule.
26. The system of claim 24 wherein the instructions embodied in the at least one computer readable memory further cause said at least one processor to:
compare the results to a plurality of known incorrect results;
discard any of said results that matches any one of said known incorrect results.
27. The system of claim 24 wherein the instructions embodied in the at least one computer readable memory further cause said at least one processor to:
obtain at least one other indicator for each of the results, said at least one other indicator indicating a degree of success of said comparison of results for each of the results.
28. The system of claim 27 wherein the instructions embodied in the at least one computer readable memory further cause said at least one processor to:
create a data structure, said data structure comprising:
said results;
said desired result obtained by applying said selected at least one rule;
one further other indicator indicating said ranking according to confidence for each of said results;
said at least one other indicator for each of the results.
29. The system of claim 24 wherein said observationally derived data comprises data derived from addressed pieces and wherein said results comprise alphanumerical codes.
30. The system of claim 29 wherein the instructions embodied in the at least one computer readable memory further cause said at least one processor to:
obtain at least one indicator for one of said alphanumerical codes, said one of said alphanumerical codes resulting from applying one of said procedures to specific observationally derived data, said at least one indicator indicating the number of alphanumerical codes resulting from applying other ones of said plurality of procedures to said specific observationally derived data that are congruent to said one of said alphanumerical codes for a given number digits of said one of said alphanumerical codes.
31. The system of claim 24 wherein the instructions embodied in the at least one computer readable memory further cause said at least one processor to:
generate said at least one rule.
32. The method of claim 1 wherein said algorithm is implemented using techniques of artificial intelligence.
33. The computer program product of claim 16 wherein said algorithm is implemented using techniques of artificial intelligence.
34. The computer program product of claim 22 wherein said algorithm is implemented using techniques of artificial intelligence.
35. The system of claim 24 wherein said algorithm is implemented using techniques of artificial intelligence.
36. The system of claim 31 wherein said algorithm is implemented using techniques of artificial intelligence.