DOCUMENT DATE AS A RANKING FACTOR FOR CRAWLING

• US 20070250486A1
• Filed: 04/18/2007
• Published: 10/25/2007
• Est. Priority Date: 03/01/2006
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Liao, Ciya, Krishnaprasad, Muralidhar, Bhavsar, Meeten, Chang, Thomas
• Granted Patent: US 7,725,465 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06F 21/31 User authentication
• CPC Class Codes: G06F 21/6227 where protection concerns the structure of data, e.g. records, types, queries
• CPC Class Codes: H04L 63/08 for authentication of entities cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32
• CPC Class Codes: H04L 63/0815 providing single-sign-on or federations
• CPC Class Codes: H04L 63/083 using passwords cryptographic mechanisms or cryptographic arrangements for entity authentication using a predetermined code H04L9/3226
• CPC Class Codes: H04L 63/102 Entity profiles

Abstract

A flexible and extensible architecture allows for secure searching across an enterprise. Such an architecture can provide a simple Internet-like search experience to users searching secure content inside (and outside) the enterprise. The architecture allows for the crawling and searching of a variety or sources across an enterprise, regardless of whether any of these sources conform to a conventional user role model. The architecture further allows for security attributes to be submitted at query time, for example, in order to provide real-time secure access to enterprise resources. The user query also can be transformed to provide for dynamic querying that provides for a more current result list than can be obtained for static queries.

Claims

1. A method in a computer system of improving ranking results for a data crawl, comprising:

selecting an attribute to be used in adjusting the ranking results for a query;

running a query against at least one data source and generating a set of query results, each query result having a relevancy score relating to the query;

adjusting the relevancy scores of the query results based on the selected attribute; and

returning the query results with the adjusted relevancy scores in response to the query.

2. A method according to claim 1, further comprising:

receiving the query from a user; and

displaying the returned query results to the user in an order based on the adjusted relevancy scores.

3. A method according to claim 1, further comprising:

selecting multiple attributes to be used in adjusting the ranking results.

4. A method according to claim 1, wherein:

the selected attribute is selected from the group consisting of creation date, modified date, and occurrence data.

5. A method according to claim 1, further comprising:

storing the query results to a hit list before adjusting the relevancy scores.

6. A method according to claim 5, wherein:

adjusting the relevancy scores includes executing a hit list re-rank procedure that adjusts the relevancy score of each query result in the hit list.

7. A method according to claim 1, wherein:

the data being queried is selected from the group consisting of documents, Web pages, email messages, and calendar events.

8. A method according to claim 1, wherein:

the query results are ordered by keys.

9. A method according to claim 8, wherein:

the keys include data for the selected attribute.

10. A method according to claim 8, wherein:

the keys take the form of;

(max_relevant_score−

relevant_score)*1000000+recency*10000+sequence

wherein max_relevant_score is the maximum relevancy score, relevant_score is the relevancy score determined for a current query result, recency is a difference in time, and sequence is a sequence number from an original hit list, and

wherein relevant_score, recency, and sequence are all integers.

11. A method according to claim 10, wherein:

recency is calculated using the following;

recency=|sysdate-last_modified_date|,—

in number of days

when recency>

30 then recency=30+recency/30,

when recency>

99 then recency=99,

when recency<

0 then recency=99, and

wherein sysdate is the current system date.

12. A computer program product embedded in a computer readable medium for improving ranking results for a data crawl, comprising:

program code for selecting an attribute to be used in adjusting the ranking results for a query;

program code for running a query against at least one data source and generating a set of query results, each query result having a relevancy score relating to the query;

program code for adjusting the relevancy scores of the query results based on the selected attribute; and

program code for returning the query results with the adjusted relevancy scores in response to the query.

13. A computer program product according to claim 12, further comprising:

program code for receiving the query from a user; and

program code for displaying the returned query results to the user in an order based on the adjusted relevancy scores.

14. A computer program product according to claim 12, further comprising:

program code for selecting multiple attributes to be used in adjusting the ranking results.

15. A computer program product according to claim 12, wherein:

the selected attribute is selected from the group consisting of creation date, modified date, and occurrence data.

16. A computer program product according to claim 12, further comprising:

program code for storing the query results to a hit list before adjusting the relevancy scores.

17. A computer program product according to claim 16, wherein:

program code for adjusting the relevancy scores includes program code for executing a hit list re-rank procedure that adjusts the relevancy score of each query result in the hit list.

18. A computer program product according to claim 12, further comprising:

program code for ordering the query results by keys.

19. A system for improving ranking results for a data crawl, comprising:

at least one data source operable to store queryable data; and

a processing device in communication with the at least one data source and operable to execute a query against the queryable data, the processing device being further operable to receive a selection of an attribute to be used in adjusting the ranking results for a query and to adjust the relevancy scores of query results for an executed query based on the selected attribute, the processing device being further operable to return the query results with the adjusted relevancy scores in response to the query.

20. A system according to claim 19, wherein:

the processing device is further operable to receive the query from a user and display the returned query results to the user in an order based on the adjusted relevancy scores.

21. A system according to claim 19, wherein:

the selected attribute is selected from the group consisting of creation date, modified date, and occurrence data.

22. A system according to claim 19, wherein:

the processing device is further operable to store the query results to a hit list before adjusting the relevancy scores.

23. A system according to claim 19, wherein:

the processing device is further operable to adjust the relevancy scores by executing a hit list re-rank procedure that adjusts the relevancy score of each query result in the hit list.

24. A system according to claim 23, wherein:

the processing device is further operable to write the query results to an output buffer before executing the hit list re-rank procedure.

25. A system according to claim 19, wherein:

the processing device is further operable to order the query results by keys.

26. A system according to claim 25, wherein:

the keys include data for the selected attribute.

27. A system according to claim 25, wherein:

the keys take the form of;

(max_relevant_score−

relevant_score)*1000000+recency*10000+sequence

wherein max_relevant_score is the maximum relevancy score, relevant_score is the relevancy score determined for a current query result, recency is a difference in time, and sequence is a sequence number from an original hit list, and

wherein relevant_score, recency, and sequence are all integers.

28. A system according to claim 27, wherein:

recency is calculated using the following;

recency=|sysdate-last_modified_date|,—

in number of days

when recency>

30 then recency=30+recency/30,

when recency>

99 then recency=99,

when recency<

0 then recency=99, and

wherein sysdate is the current system date.