1. A method comprising:
digitally signing a first load module with a first digital signature designating the first load module as having a first security class;
digitally signing a second load module with a second digital signature, the second digital signature designating the second load module as having a second security class relatively higher than the first security class; and
distributing the first load module and the second load module to an execution environment, the execution environment being operable to determine a security class of the first load module based on the first digital signature and a security class of the second load module based on the second digital signature.
2. The method of claim 1, in which the first load module, when executed by an electronic appliance, is configured to cause the electronic appliance to perform at least one action selected from the group consisting of: record an aspect of usage of a piece of electronic content, prevent a user from making a copy of a piece of electronic content, charge a user a fee for viewing a piece of electronic content, enable the electronic appliance to perform a financial transaction, and enable the electronic appliance to play a piece of electronic content.
3. The method of claim 1, further comprising:
testing the first load module;
wherein the step of digitally signing the first load module is conditionally performed based, at least in part, on a result of the testing step.
4. The method of claim 3, in which testing the first load module comprises testing the first load module to determine if it does what it is supposed to do.
5. The method of claim 3, in which testing the first load module comprises determining whether the first load module is operable to compromise or harm a system upon which it is executed.
6. The method of claim 3, in which testing the first load module comprises determining whether the first load module performs as specified by a specification.
7. The method of claim 3, in which testing the first load module comprises determining whether the first load module is a virus.
8. The method of claim 1, further comprising:
encrypting the first load module.
9. The method of claim 1, further comprising:
securely distributing a first public key to the execution environment, the first public key being configured for use in authenticating the first digital signature.
10. The method of claim 1, further comprising:
distributing the first digital signature and the second digital signature to the execution environment.
11. A computer-readable medium comprising program code, the program code being operable, when executed by a computer system, to cause the computer system to perform steps comprising:
digitally signing a first load module with a first digital signature designating the first load module as having a first security class;
digitally signing a second load module with a second digital signature, the second digital signature designating the second load module as having a second security class relatively higher than the first security class; and
distributing the first load module and the second load module to an execution environment, the execution environment being operable to determine a security class of the first load module based on the first digital signature and a security class of the second load module based on the second digital signature.
12. The computer-readable medium of claim 11, further including program code that is operable, when executed by the computer system, to cause the computer system to perform the step of:
testing the first load module;
wherein the program code for performing the step of digitally signing the first load module is conditionally executed based, at least in part, on a result of the testing step.
13. The computer-readable medium of claim 12, in which the program code for testing the first load module comprises program code that is operable, when executed by the computer system, to cause the computer system to determine if the first load module does what it is supposed to do.
14. The computer-readable medium of claim 12, in which the program code for testing the first load module comprises program code that is operable, when executed by the computer system, to cause the computer system to determine whether the first load module is operable to compromise or harm a system upon which it is executed.
15. The computer-readable medium of claim 12, in which the program code for testing the first load module comprises program code that is operable, when executed by the computer system, to cause the computer system to determine whether the first load module performs as specified by a specification.
16. The computer-readable medium of claim 12, in which the program code for testing the first load module comprises program code that is operable, when executed by the computer system, to cause the computer system to determine whether the first load module is a virus.
17. The computer-readable medium of claim 11, further including program code that is operable, when executed by the computer system, to cause the computer system to perform the step of:
encrypting the first load module.
18. The computer-readable medium of claim 11, further including program code that is operable, when executed by the computer system, to cause the computer system to perform the step of:
securely distributing a first public key to the execution environment, the first public key being configured for use in authenticating the first digital signature.
19. The computer-readable medium of claim 11, further including program code that is operable, when executed by the computer system, to cause the computer system to perform the step of:
distributing the first digital signature and the second digital signature to the execution environment.
20. A system comprising:
means for digitally signing a first load module with a first digital signature designating the first load module as having a first security class;
means for digitally signing a second load module with a second digital signature, the second digital signature designating the second load module as having a second security class relatively higher than the first security class; and
means for distributing the first load module and the second load module to an execution environment, the execution environment being operable to determine a security class of the first load module based on the first digital signature and a security class of the second load module based on the second digital signature.
21. The system of claim 20, further comprising:
means for testing the first load module.
22. The system of claim 21, in which the means for testing comprises means for testing the first load module to determine if it does what it is supposed to do.
23. The system of claim 21, in which the means for testing comprises means for determining whether the first load module is operable to compromise or harm a system upon which it is executed.
24. The system of claim 21, in which the means for testing comprises means for determining whether the first load module performs as specified by a specification.
25. The system of claim 21, in which the means for testing comprises means for determining whether the first load module is a virus.
26. The system of claim 20, further comprising:
means for encrypting the first load module.
27. The system of claim 20, further comprising:
means for securely distributing a first public key to the execution environment, the first public key being configured for use in authenticating the first digital signature.
28. The system of claim 20, further comprising:
means for distributing the first digital signature and the second digital signature to the execution environment.
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 of database management, comprising the steps of:
generating characteristic rules based upon data definition information and data, the data definition information including items specifying analysis and conditions;
generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized based upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension;
selecting one of the characteristic rules via a predetermined user-interface;
displaying a portion of the multidimensional database that is corresponding to the selected one of the characteristic rules, the displayed portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension;
modifying the condition items through the predetermined user-interface;
displaying another portion of the multidimensional database that is corresponding to the modified condition items through the predetermined user-interface;
extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list;
generating a speculation model base upon the data, the selected segment and the speculation data list; and
outputting speculation results based upon the speculation model and the speculation data list.
2. The method of database management according to claim 1 wherein the data is customer data for maintaining a predetermined subscribed service while the analysis dimension is probability of cancellation of the predetermined subscribed service.
3. The method of database management according to claim 1 wherein said modifying the condition items is accomplished by displaying a predetermined set of the condition items in a pull-down menu and selecting one of the condition items by a pointing device.
4. The method of database management according to claim 1 wherein said generating the speculation model further including additional steps of:
dividing the speculation data list into candidate model data and verification data;
generating candidate speculation models based upon inputs as specified by combinations of the conditions in the data definition information and outputs as specified by the analysis in the data definition information;
verifying each of the candidate speculation models by extracting information from the candidate model data according to the candidate speculation model and speculating based upon the verification data;
evaluating the candidate speculation models based upon said verifying to generate evaluation values; and
selecting the speculation model from the candidate speculation models based upon the evaluation values.
5. The method of database management according to claim 4 wherein the evaluation values are mean square errors.
6. A method of database management, comprising the steps of:
generating characteristic rules based upon data definition information and data, the data definition information including items specifying analysis and conditions;
generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized bated upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension;
selecting one of the characteristic rules via a predetermined user-interface;
displaying a portion of the multidimensional database that is corresponding to the selected one of the characteristic rules through the predetermined user-interface, the displayed portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension;
modifying the condition items through the predetermined user-interface;
displaying another portion of the multidimensional database that is corresponding to the modified condition items through the predetermined user-interface;
extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list;
generating a speculation model based upon the data, the selected segment and the speculation data list; and
outputting speculation results based upon the speculation model and the speculation data list, wherein the speculation results include the selected segment.
7. A system for data mining a database comprising:
a data storage unit for storing data definition information and data;
a characteristic rule generation unit connected to said data storage unit for generating characteristic rules based upon the data definition information and the data, the data definition information including items specifying analysis and conditions, the characteristic rules being stored in said data storage unit;
a segment selection unit connected to said data storage unit for generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized based upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension, the multidimensional database being stored in said data storage unit;
a user interface unit connected to said data storage unit for providing a predetermined user-interface for selecting one of the characteristic rules and for modifying the condition items;
a processing unit connected to said storage unit and said user interface unit for outputting to said storage unit a first portion of the multidimensional database that is corresponding to the selected one of the characteristic rules, the first portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension, said processing unit also outputting a second portion of the multidimensional database that is corresponding to the modified condition items;
a displaying unit connected to said user-interface unit, said processing unit and said storage unit for displaying the first portion of the multidimensional database and the second portion of the multidimensional database through the predetermined user-interface; and
a speculation processing unit connected to said storage unit and said processing unit for extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list, said speculation processing unit generating a speculation model based upon the data, the selected segment and the speculation data list, said speculation processing unit outputting speculation results based upon the speculation model and the speculation data list.
8. The system for data mining a database according to claim 7 wherein the data is customer data for maintaining a predetermined subscribed service while the analysis dimension is probability of cancellation of the predetermined subscribed service.
9. The system for data mining a database according to claim 7 wherein said user interface unit modifying the condition items by displaying a predetermined set of the condition items in a pull-down menu and selecting one of the condition items by a pointing device.
10. The system for data mining a database according to claim 7 wherein said speculation processing unit further comprises:
a data dividing unit for dividing the speculation data list into candidate model data and verification data;
a candidate model generation unit for generating candidate speculation models based upon inputs as specified by combinations of the conditions in the data definition information and outputs as specified by the analysis in the data definition information; and
a verification unit for connected to said candidate model generation unit for verifying each of the candidate speculation models by extracting information from the candidate model data according to the candidate speculation model and speculating based upon the verification data, said verification unit evaluating the candidate speculation models based upon said verifying to generate evaluation values and selecting the speculation model from the candidate speculation models based upon the evaluation values.
11. The system for data mining a database according to claim 10 wherein the evaluation values are mean square errors.
12. A system for data mining a database comprising:
a data storage unit for storing data definition information and data;
a characteristic rule generation unit connected to said data storage unit for generating characteristic rules based upon the data definition information and the data, the data definition information including items specifying analysis and conditions, the characteristic rules being stored in said data storage unit;
a segment selection unit connected to said data storage unit for generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized based upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension, the multidimensional database being stored in said data storage unit;
a user interface unit connected to said data storage unit for providing a predetermined user-interface for selecting one of the characteristic rules and for modifying the condition items;
a processing unit connected to said storage unit and said user interface unit for outputting to said storage unit a first portion of the multidimensional database that is corresponding to the selected one of the characteristic rules, the first portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension, said processing unit also outputting a second portion of the multidimensional database that is corresponding to the modified condition items;
a displaying unit connected to said user interface unit, said processing unit and said storage unit for displaying the first portion of the multidimensional database and the second portion of the multidimensional database through the predetermined user-interface; and
a speculation processing unit connected to said storage unit and said processing unit for extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list, said speculation processing unit generating a speculation model based upon the data, the selected segment and the speculation data list, said speculation processing unit outputting speculation results based upon the speculation model and the speculation data list, wherein the speculation results include the selected segment.
13. A storage medium for storing computer executable instructions for managing a database, the computer executable instructions performing the steps of:
generating characteristic rules based upon data definition information and data, the data definition information including items specifying analysis and conditions;
generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized based upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension;
selecting one of the characteristic rules via a predetermined user-interface;
displaying a portion of the multidimensional database that is corresponding to the selected one of the characteristic rules through the predetermined user-interface, the displayed portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension;
modifying the condition items through the predetermined user-interface;
displaying another portion of the multidimensional database that is corresponding to the modified condition items through the predetermined user-interface;
extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list;
generating a speculation model base upon the data, the selected segment and the speculation data list; and
outputting speculation results based upon the speculation model and the speculation data list.
14. The storage medium for storing computer executable instructions according to claim 13 wherein the data is customer data for maintaining a predetermined subscribed service while the analysis dimension is probability of cancellation of the predetermined subscribed service.
15. The storage medium for storing computer executable instructions according to claim 13 wherein said modifying the condition items is accomplished by displaying a predetermined set of the condition items in a pull-down menu and selecting one of the condition items by a pointing device.
16. The storage medium for storing computer executable instructions according to claim 13 wherein said generating the speculation model further including additional steps of:
dividing the speculation data list into candidate model data and verification data;
generating candidate speculation models based upon inputs as specified by combinations of the conditions in the data definition information and outputs as specified by the analysis in the data definition information;
verifying each of the candidate speculation models by extracting information from the candidate model data according to the candidate speculation model and speculating based upon the verification data;
evaluating the candidate speculation models based upon said verifying to generate evaluation values; and
selecting the speculation model from the candidate speculation models based upon the evaluation values.
17. The storage medium for storing computer executable instructions according to claim 16 wherein the evaluation values are mean square errors.
18. A storage medium for storing computer executable instructions for managing a database, the computer executable instructions performing the steps of:
generating characteristic rules based upon data definition information and data, the data definition information including items specifying analysis and conditions;
generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized bated upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension;
selecting one of the characteristic rules via a predetermined user-interface;
displaying a portion of the multidimensional database that is corresponding to the selected one of the characteristic rules through the predetermined user-interface, the displayed portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension;
modifying the condition items through the predetermined user-interface;
displaying another portion of the multidimensional database that is corresponding to the modified condition items through the predetermined user-interface;
extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list;
generating a speculation model based upon the data, the selected segment and the speculation data list; and
outputting speculation results based upon the speculation model and the speculation data list, wherein the speculation results include the selected segment.