Methods and systems for node ranking based on DNS session data

• US 7,593,935 B2
• Filed: 10/19/2006
• Issued: 09/22/2009
• Est. Priority Date: 10/19/2006
• Status: Active Grant

• Current Assignee: Bushnell Hawthorne, LLC
• Original Assignee: Paxfire, Inc.
• Inventors: SULLIVAN, Alan T.
• Primary Examiner(s): Corrielus, Jean M
• Application Number:
  US11/550,975
• Publication Number:
  US 20080097980A1
• Time in Patent Office: 1,069 Days
• Field of Search: 707/3, 707/10, 707/5, 707/7, 707/200, 707/102, 705/52, 715/207, 715/230, 715/256, 709/201, 709/203, 709/225
• US Class Current: 1/1
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99937 Sorting

Abstract

Systems and methods for ranking pages and/or hosts in a faster and more relevant manner are provided. Systems and methods for ranking pages and/or hosts based on session data and/or traffic data are also provided. According to the invention, session maps can be created using DNS and/or ISP data. Systems and methods for ranking pages and/or hosts for the purpose of doing business are also provided.

Claims

1. A method for ranking pages using DNS data, the method comprising:

obtaining ISP DNS data for a particular session;

identifying one or more sequences of host requests based on the ISP DNS data; and

using a computing device to rank ranking pages and/or hosts based at least in part on the identified sequences of hosts,

wherein ranking pages comprises;

calculating a global host rank vector for hosts in the session;

calculating a local page rank vector for pages of each host in the session by applying the page rank model to only the hosts visited, wherein the local page rank vectors are calculated prior to, subsequent to, or concurrently with the calculation of the global host rank vector, wherein said local Page rank vectors are pre-computed for certain hosts and combined with dynamically obtained host rank vectors to provide a global page rank on-the-fly; and

approximating a global page rank vector based on a combination of the global host rank vector and local page rank vector based a dynamic traffic flow model and/or static page ranking model.

2. The method of claim 1, further comprising creating one or more session vectors from the one or more sequences of host requests and creating a session map from the one or more session vectors.

3. The method of claim 2, further comprising using the session vectors and/or session map to track and/or predict user behavior.

4. The method of claim 1, wherein ranking pages comprises ranking pages based at least in part on a dynamic traffic flow model and/or a static page ranking model.

5. The method of claim 1, further comprising providing the ranked results to a user in response to a query.

6. A computer program residing on one or more computer readable storage media and comprising instructions when executed by a computer causes the computer to perform the method for ranking pages using DNS data, the method comprising:

obtaining ISP DNS data for a particular session;

identifying one or more sequences of host requests based on the ISP DNS data; and

using a computing device to rank ranking pages and/or hosts based at least in part on the identified sequences of hosts,

wherein ranking pages comprises;

calculating a global host rank vector for hosts in the session;

calculating a local page rank vector for pages of each host in the session by applying the page rank model to only the hosts visited, wherein the local page rank vectors are calculated prior to, subsequent to, or concurrently with the calculation of the global host rank vector, wherein said local Page rank vectors are pre-computed for certain hosts and combined with dynamically obtained host rank vectors to provide a global page rank on-the-fly; and

approximating a global page rank vector based on a combination of the global host rank vector and local page rank vector based a dynamic traffic flow model and/or static page ranking model.

7. An Internet appliance comprising:

means for obtaining ISP DNS data for a particular session;

means for identifying one or more sequences of host requests based on the ISP DNS data;

means for ranking pages and/or hosts based at least in part on the identified sequences of hosts,

wherein the means for ranking pages comprises;

means for calculating a global host rank vector for hosts in the session;

means for calculating a local page rank vector for pages of each host in the session by applying the page rank model to only the hosts visited, wherein the local page rank vectors are calculated prior to, subsequent to, or concurrently with the calculation of the global host rank vector, wherein said local Page rank vectors are pre-computed for certain hosts and combined with dynamically obtained host rank vectors to provide a global page rank on the fly; and

means for approximating a global page rank vector based on a combination of the global host rank vector and local page rank vector based a dynamic traffic flow model and/or static page ranking model.

8. The Internet appliance of claim 7, further comprising:

means for creating one or more session vectors from the one or more sequences of host requests; and

means for creating a session map from the one or more session vectors.

9. The Internet appliance of claim 8, further comprising:

means for tracking and/or predicting user behavior based on the session vectors and/or session map.

10. The Internet appliance of claim 7, further comprising:

means for ranking pages and/or hosts in the session using a dynamic traffic flow and/or static page ranking model.

11. The Internet appliance claim 7, wherein the means for calculating a global host rank vector uses a traffic flow model.

12. The Internet appliance of claim 7, further comprising:

means for providing the ranked results to a user in response to a query.

13. An Internet appliance comprising:

a processor configured to obtain ISP DNS data for a particular session;

a processor configured to identify one or more sequences of host requests based on the ISP DNS data;

a processor configured to rank pages and/or hosts based at least in part on the identified sequences of hosts,

wherein the processor configured to rank pages and/or hosts is further configured to;

calculate a global host rank vector for hosts in the session;

calculate a local page rank vector for pages of each host in the session by applying the page rank model to only the hosts visited, wherein the local page rank vectors are calculated prior to, subsequent to, or concurrently with the calculation of the global host rank vector, wherein said local Page rank vectors are pre-computed for certain hosts and combined with dynamically obtained host rank vectors to provide a global page rank on the fly; and

approximate a global page rank vector based on a combination of the global host rank vector and local page rank vector based a dynamic traffic flow model and/or static page ranking model.

14. The Internet appliance of claim 13, wherein the processor configured to rank pages and/or hosts is further configured to calculate the global host rank vector using a traffic flow model.

15. A system for ranking pages by one or more ISPs, said system comprising:

one or more Internet appliances, each Internet appliance comprising;

one or more processors configured to collect and/or obtain ISP DNS data from one or more ISP;

one or more processors configured to rank search results based at least in part on the ISP DNS data combined from one or more ISP,

wherein the processors configured to rank pages search results are configured to;

calculate a global host rank vector for hosts in the session;

calculate a local page rank vector for pages of each host in the session by applying the page rank model to only the hosts visited, wherein the local page rank vectors are calculated prior to, subsequent to, or concurrently with the calculation of the global host rank vector, wherein said local Page rank vectors are pre-computed for certain hosts and combined with dynamically obtained host rank vectors to provide a global page rank on the fly; and

approximate a global page rank vector based on a combination of the global host rank vector and local page rank vector based a dynamic traffic flow model and/or static page ranking model.

16. computer readable storage medium having instructions residing thereon when executed by a computer causes the computer to perform the method for ranking pages using DNS data, the method comprising:

obtaining and/or combining ISP DNS data from one or more ISP;

identifying one or more sequences of host requests using the ISP DNS data;

ranking pages and/or hosts based at least in part on the identified sequences of hosts, wherein ranking pages comprises;

calculating a global host rank vector for hosts in the session;

calculating a local page rank vector for pages of each host in the session by applying the page rank model to only the hosts visited, wherein the local page rank vectors are calculated prior to, subsequent to, or concurrently with the calculation of the global host rank vector, wherein said local Page rank vectors are pre-computed for certain hosts and combined with dynamically obtained host rank vectors to provide a global page rank on-the-fly; and

approximating a global page rank vector based on a combination of the global host rank vector and local page rank vector based a dynamic traffic flow model and/or static page ranking model.

17. The computer readable storage medium of claim 16, further comprising instructions for:

ranking pages and/or hosts using a dynamic traffic flow and/or static page ranking model.

18. The computer readable storage medium of claim 16, further comprising instructions for:

creating one or more session vectors and/or session maps from the sequences of host requests.

19. The computer readable storage medium of claim 18, further comprising instructions for:

tracking and/or predicting user behavior based at least in part on information contained in the session vectors and/or session maps.

20. The computer readable storage medium of claim 16, further comprising instructions for:

ranking pages based on a combination of the one or more identified sequences and advertiser information.