RANKING METHOD AND SYSTEM

• US 20140214826A1
• Filed: 03/31/2014
• Published: 07/31/2014
• Est. Priority Date: 01/29/2013
• Status: Abandoned Application

• Current Assignee: Tencent Technology Company Limited (Tencent Holdings Limited)
• Original Assignee: Tencent Technology Company Limited (Tencent Holdings Limited)
• Inventors: ZHANG, YASHENG
• Application Number:
  US14/230,096
• Publication Number:
  US 20140214826A1
• Time in Patent Office: Days
• US Class Current: 707/732
• CPC Class Codes: G06F 16/24578 using ranking

Abstract

Various embodiments provide ranking methods and systems. The ranking method can be implemented by a computer system. In an exemplary method, real-time data can be obtained. A total user number of the real-time data can be counted. A distribution pattern of user number in one or more data value intervals can be obtained from the real-time data. The total user number and the distribution pattern can then be stored as intermediate data. A ranking query request of a user and an actual data value of the user can be received. A ranking of the user can be calculated according to the actual data value of the user and the intermediate data.

Claims

1. A ranking method, implemented by a computer system, comprising:

obtaining real-time data;

counting a total user number of the real-time data;

obtaining, from the real-time data, a distribution pattern of user number in one or more data value intervals;

storing the total user number and the distribution pattern as intermediate data;

receiving a ranking query request of a user and an actual data value of the user; and

calculating a ranking of the user according to the actual data value of the user and the intermediate data.

2. The method according to claim 1, wherein the real-time data include data obtained by sampling.

3. The method according to claim 1, wherein the obtaining of the distribution pattern of user number in the one or more data value intervals includes:

obtaining a minimum data value and a maximum data value of the real-time data.

4. The method according to claim 3, wherein the calculating of the ranking uses a mathematical rule of probability distribution including a formula:

P=(m(n2−

n)/(n2−

n1))+1, wherein;

P is the ranking of the user, m is the total user number of the real-time data, n1 is the minimum data value of the real-time data, n2 is the maximum data value of the real-time data, and n is the actual data value of the user.

5. The method according to claim 1, wherein the obtaining of the distribution pattern of user number in the one or more data value intervals includes:

obtaining a minimum data value and a maximum data value of the real-time data;

splitting the real-time data between the minimum data value and the maximum data value sequentially into a plurality of attribute value intervals;

obtaining a relative minimum data value and a relative maximum data value of each of the plurality of attribute value intervals; and

obtaining the user number respectively between the minimum data value of the real-time data and the relative maximum data value of the each of the plurality of attribute value intervals.

6. The method according to claim 5, wherein the calculating of the ranking uses a mathematical rule of probability distribution including a formula:

P=(m−

iy+(ky−

n)(iy−

ix)/(ky−

kx))+1, wherein;

P is the ranking of the user, m is the total user number of the real-time data, ix is a user number between the minimum data value of the real-time data and a relative minimum data value of an attribute value interval that the user belongs to, iy is a user number between the minimum data value of the real-time data and a relative maximum data value of the attribute value interval that the user belongs to, kx is the relative minimum data value of the attribute value interval that the user belongs to, ky is the relative maximum data value of the attribute value interval that the user belongs to, and n is the actual data value of the user.

7. A ranking system, comprising:

a data-obtaining module configured to obtain real-time data;

a statistics module configured to count a total user number of the real-time data;

a distribution-pattern-obtaining module configured to obtain a distribution pattern of user number of the real-time data in one or more data value intervals;

a storage module configured to store intermediate data, wherein the intermediate data includes the total user number and the distribution pattern;

an interaction module configured to communicate with user terminals; and

a calculation module configured to calculate a ranking of a user according to an actual data value of the user and the intermediate data.

8. The system according to claim 7, wherein the real-time data are obtained by sampling.

9. The system according to claim 7, wherein the distribution-pattern-obtaining module includes:

a data-value-obtaining unit configured to obtain a minimum data value and a maximum data value of the real-time data.

10. The system according to claim 9, wherein the calculation module is configured to calculate the ranking using a mathematical rule of probability distribution including a formula:

P=(m(n2−

n)/(n2−

n1))+1, wherein;

P is the ranking of the user, m is the total user number of the real-time data, n1 is the minimum data value of the real-time data, n2 is the maximum data value of the real-time data, and n is the actual data value of the user.

11. The system according to claim 7, wherein the distribution-pattern-obtaining module includes:

a data-value-obtaining unit configured to obtain a minimum data value and a maximum data value of the real-time data;

an interval-splitting unit configured to split the real-time data between the minimum data value and the maximum data value sequentially into a plurality of attribute value intervals;

a relative-data-value-obtaining unit configured to obtain a relative minimum data value and a relative maximum data value of each of the plurality of attribute value intervals; and

an interval-user-statistics unit configured to obtain the user number respectively between the minimum data value of the real-time data and the relative maximum data value of the each of the plurality of attribute value intervals.

12. The system according to claim 11, wherein the calculation module is configured to calculate the ranking using a mathematical rule of probability distribution including a formula:

P=(m−

iy+(ky−

n)(iy−

ix)/(ky−

kx))+1, wherein;

P is the ranking of the user, m is the total user number of the real-time data, ix is a user number between the minimum data value of the real-time data and a relative minimum data value of an attribute value interval that the user belongs to, iy is a user number between the minimum data value of the real-time data and a relative maximum data value of the attribute value interval that the user belongs to, kx is the relative minimum data value of the attribute value interval that the user belongs to, ky is the relative maximum data value of the attribute value interval that the user belongs to, and n is the actual data value of the user.

13. A non-transitory computer-readable medium having computer program for, when being executed by a processor, performing a ranking method comprising:

obtaining real-time data;

counting a total user number of the real-time data;

obtaining, from the real-time data, a distribution pattern of user number in one or more data value intervals;

storing the total user number and the distribution pattern as intermediate data;

receiving a ranking query request of a user and an actual data value of the user; and

calculating a ranking of the user according to the actual data value of the user and the intermediate data.

14. The non-transitory computer-readable medium according to claim 13, wherein the real-time data include data obtained by sampling.

15. The non-transitory computer-readable medium according to claim 13, wherein the obtaining of the distribution pattern of user number in the one or more data value intervals includes:

obtaining a minimum data value and a maximum data value of the real-time data.

16. The method according to claim 15, wherein the calculating of the ranking uses a mathematical rule of probability distribution including a formula:

P=(m(n2−

n)/(n2−

n1))+1, wherein;

P is the ranking of the user, m is the total user number of the real-time data, n1 is the minimum data value of the real-time data, n2 is the maximum data value of the real-time data, and n is the actual data value of the user.

17. The non-transitory computer-readable medium according to claim 13, wherein the obtaining of the distribution pattern of user number in the one or more data value intervals includes:

obtaining a minimum data value and a maximum data value of the real-time data;

splitting the real-time data between the minimum data value and the maximum data value sequentially into a plurality of attribute value intervals;

obtaining a relative minimum data value and a relative maximum data value of each of the plurality of attribute value intervals; and

obtaining the user number respectively between the minimum data value of the real-time data and the relative maximum data value of the each of the plurality of attribute value intervals.

18. The non-transitory computer-readable medium according to claim 17, wherein the calculating of the ranking uses a mathematical rule of probability distribution including a formula:

P=(m−

iy+(ky−

n)(iy−

ix)/(ky−

kx))+1, wherein;

P is the ranking of the user, m is the total user number of the real-time data, ix is a user number between the minimum data value of the real-time data and a relative minimum data value of an attribute value interval that the user belongs to, iy is a user number between the minimum data value of the real-time data and a relative maximum data value of the attribute value interval that the user belongs to, kx is the relative minimum data value of the attribute value interval that the user belongs to, ky is the relative maximum data value of the attribute value interval that the user belongs to, and n is the actual data value of the user.