Data Classification

• US 20130097205A1
• Filed: 10/11/2012
• Published: 04/18/2013
• Est. Priority Date: 10/12/2011
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: He, Mei, Zhou, Jie, Kong, Baihan
• Granted Patent: US 9,280,611 B2
• Time in Patent Office: Days
• US Class Current: 707/797
• CPC Class Codes: G06F 16/2246 Trees, e.g. B+trees
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/9027 Trees
• CPC Class Codes: G06F 16/904 Browsing; Visualisation therefor for navigating the web G06F16/954; browsing optimisation for the web G06F16/957

Abstract

The present disclosure provides a method and an apparatus for storing data classification structure. Based on an initial classification structure tree, a reorganized classification structure tree that stores data classifications structure differently from that of the initial classification structure is generated. As the reorganized classification structure tree is flexible, when maintaining data and searching data by using the search engine, the present techniques may quickly find the desired data, thereby reducing the heavy burden of the search engine under the conventional techniques to conduct data search and high pressure of conducting data maintenance and data search. Further, the present techniques may not only reduce the burden of the search engine, but also relieve the pressure for maintaining data and searching data.

Claims

1. A method performed by one or more processors configured with computer-executable instructions, the method comprising:

obtaining an initial classification structure tree that stores a data classification structure, the initial classification structure tree including an initial root node and at least one child node;

generating a reorganized classification structure tree that stores another data classification structure different from and at least based on the initial classification structure tree; and

storing the reorganized classification structure tree.

2. The method as recited in claim 1, wherein the generating the reorganized classification structure tree comprises:

determining the initial root node or a child node in the initial classification structure tree as a root node of the reorganized classification structure tree;

determining at least one node among remaining nodes of the initial classification structure tree other than the determined initial root node or child node as one or more child nodes of the reorganized classification structure tree;

using corresponding data as one or more leaf nodes of the reorganized classification structure tree; and

generating the reorganized classification structure tree based on the determined root node, the one or more child nodes, and the one or more leaf nodes of the reorganized classification structure tree.

3. The method as recited in claim 1, wherein generating the reorganized classification structure tree comprises generating the reorganized classification structure tree based on the initial classification structure tree and another initial classification structure tree.

4. The method as recited in claim 1, wherein generating the reorganized classification structure tree comprises generating the reorganized classification structure tree and another reorganized classification structure tree based on the initial classification structure tree.

5. The method as recited in claim 1, wherein generating the reorganized classification structure tree comprises generating the reorganized classification structure tree and another reorganized classification structure tree based on the initial classification structure tree and another initial classification structure tree.

6. The method as recited in claim 5, wherein the initial classification structure tree and the another initial classification structure tree are a same type.

7. The method as recited in claim 5, wherein the initial classification structure tree and the another initial classification structure tree are different types.

8. The method as recited in claim 1, wherein the initial classification structure tree is generated based on a standard classification structure tree when data is published.

9. The method as recited in claim 1, wherein:

the initial root node of the initial classification structure tree represents data source characteristics of the initial classification structure tree; and

at least one child node of the initial classification structure tree describes classification characteristics of a corresponding classification in the initial classification structure tree.

10. The method as recited in claim 1, wherein the root node of the reorganized classification structure tree represents data source characteristics of the reorganized classification structure tree.

11. A method performed by one or more processors configured with computer-executable instructions, the method comprising:

displaying an initial root node of an initial classification structure and a root node of each reorganized classification structure tree as guidance information of data source characteristics at a display interface;

based on data source characteristics selected by a user, determining a classification structure tree with a root node representing the selected data source characteristics; and

providing a level-by-level guidance to the user based on the determined classification structure tree.

12. The method as recited in claim 11, wherein the providing the level-by-level guidance to the user based on the determined classification structure tree comprises providing a level-by-level guidance to the user based on the determined classification structure tree until desired data is selected by the user.

13. The method as recited in claim 11, wherein the initial classification structure tree is generated based on a standard classification structure tree when data is published.

14. The method as recited in claim 11, wherein:

the initial root node of the initial classification structure tree represents data source characteristics of the initial classification structure tree; and

at least one child node of the initial classification structure tree describes classification characteristics of a corresponding classification in the initial classification structure tree.

15. The method as recited in claim 11, wherein the root node of the reorganized classification structure tree represents data source characteristics of the reorganized classification structure tree.

16. A system comprising:

one or more processors; and

computer storage media having stored thereon computer-executable modules that are executable by the one or more processors, the one or more computer-executable modules comprising;

a classification dimension module that obtains an initial classification structure tree that stores a data classification structure, the initial classification structure tree including an initial root node and at least one child node;

a rule engine module that;

determines the initial root node or a child node in the initial classification structure tree as a root node of a reorganized classification structure tree;

determines at least one node among remaining nodes of the initial classification structure tree other than the determined initial root node or child node as one or more child nodes of the reorganized classification structure tree;

uses corresponding data as one or more leaf nodes of the reorganized classification structure tree; and

generates the reorganized classification structure tree based on the determined root node, the one or more child nodes, and the one or more leaf nodes of the reorganized classification structure tree; and

a classification data module that stores the initial classification structure tree and the reorganized classification structure tree.

17. The system as recited in claim 16, wherein the initial classification structure tree is generated based on a standard classification structure tree when data is published.

18. The system as recited in claim 16, wherein:

the initial root node of the initial classification structure tree represents data source characteristics of the initial classification structure tree; and

at least one child node of the initial classification structure tree describes classification characteristics of a corresponding classification in the initial classification structure tree.

19. The system as recited in claim 16, wherein the root node of the initial classification structure tree represents data source characteristics of the initial classification structure tree.

20. The system as recited in claim 16, wherein the system is a distributive system.