1. A combination lock, comprising:
a housing comprising a cover, a leg, and a resilient member;
a shackle pivotably connected to the housing and comprising a terminating end mated with an open end of the leg, an inner intermediate projection, and a spring-loaded member at the other end;
a tumbler wheel assembly comprising a plurality of tumbler wheels rotatably fastened within the housing, each tumbler wheel including a locking member having a notch, and a moveable member including a plurality of first openings for receiving the locking members, a plurality of protrusions each formed at an inner wall of the first opening, an inward projecting member including a bore and two opposite inclined grooves on an inner wall of the bore, the projecting member being urged by the resilient member;
a mechanism comprising a cylinder, a keyhole in the cylinder, a shaft projected inward, the shaft having a lengthwise flat on its surface, and a cam at a joining portion of the shaft and the cylinder; and
a spring-loaded shuttle assembly biased against the projecting member and comprising a cylinder, a head, a channel in the cylinder, the channel having a lengthwise flat on its inner wall, two opposite tabs on an outer surface of the cylinder proximate an end of the channel, a spring-loaded L-shaped member having a second opening engaged with both the cam and the spring-loaded member;
wherein:
the shaft is inserted through the second opening and the bore into the channel, a distance between one end of the shaft and a blind end of the channel is formed, and the head is engaged with the projection in an unlocked position of the lock; and
in a first lock opening operation a key is inserted into the keyhole and the key is turned to rotate the shaft, the cam, and the cylinder of the shuttle assembly for aligning the tabs with the grooves, causing the cam to contact and push the second opening laterally, causing the L-shaped member to compress the spring-loaded member for opening a gap between the leg and the shackle with the distance between one end of the shaft and the blind end of the channel being decreased to a minimum as the channel slides relative to the shaft, and moving the tabs along the grooves to project the cylinder of the shuttle assembly from one end of the bore to the other end thereof by compressing the resilient member.
2. The combination lock of claim 1, wherein in a second lock opening operation the tumbler wheels are turned to a set series of numbers for aligning the notches of the locking members with the protrusions such that pivotably pushing the shackle inward will cause the projection to press the head, move the projecting member to compress the resilient member, engage the notches of the locking members with the protrusions, and create a gap between the leg and the shackle.
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.