Network resource monitoring and measurement system and method

• US 8,495,198 B2
• Filed: 12/16/2011
• Issued: 07/23/2013
• Est. Priority Date: 08/06/1999
• Status: Expired due to Term

• Current Assignee: comScore, Inc.
• Original Assignee: comScore, Inc.
• Inventors: Sim, Lim Or, Cheong, Yee Han, Jarrett, Andrew Lawrence, Bey, Shefik, Eustace, Anthony Roger, Petit, Matthew James
• Primary Examiner(s): Nguyen, Thuong
• Application Number:
  US13/329,199
• Publication Number:
  US 20120158959A1
• Time in Patent Office: 585 Days
• Field of Search: 709/223, 709/224, 709/225, 726/10, 725/9, 725/10, 725/11, 370/352
• US Class Current: 709/223
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: H04H 60/31 Arrangements for monitoring the use made of the broadcast services
• CPC Class Codes: H04L 2012/5628 Testing
• CPC Class Codes: H04L 2012/5636 Monitoring or policing, e.g. compliance with allocated rate, corrective actions
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 43/065 related to network devices
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 67/025 for remote control or remote monitoring of applications
• CPC Class Codes: H04Q 11/0478 Provisions for broadband connections

Abstract

A method and system for analyzing and measuring multiple sources of data over a communications network (18) so as to ascertain information or usage of one or more resources, such as resource servers (2). A data collection and processing means (20) collects and processes the data sources which are forwarded to a reporting server (34) as a combined data source made available to interested parties.

Claims

1. A computer-implemented method of measuring and analysing multiple data sources over a communications network in order to ascertain information about use of one or more resources linked to said communications network, said method comprising:

obtaining, by a server, site-centric data, wherein the site-centric data corresponds to a first group of one or more monitored resources;

obtaining, by the server, user-centric data, wherein the user-centric data corresponds to a second group of one or more monitored resources;

combining, by the server, the site-centric data and the user-centric data to form a single data source corresponding to usage information of one or more resources;

processing, by the server, the site-centric data and the user-centric data to form a calibration value; and

calibrating, by the server, an unmonitored resource value corresponding to one or more unmonitored resources based on the calibration value by multiplying the unmonitored resource value by the calibration value;

wherein the processing to form a calibration value comprises;

calculating a weighting factor based on a number of users that have interactions recorded in relation to the second group of one or more monitored resources and a total number of users expected to have access to the one or more resources available through the communications network; and

multiplying the weighting factor with a number of users in the sample group that have interactions recorded in relation to the first group of one or more monitored resources to obtain a first figure for an expected number of all users to have interactions with the first group.

2. The computer-implemented method according to claim 1 comprising:

forming, by the server, when the user-centric data is obtained with respect to a group of monitored users, a sample group of monitored users;

recording, by the server, interactions of users in the sample group of monitored users; and

measuring, by the server, the recorded interactions of the users in the sample group of monitored users.

3. The computer-implemented method according to claim 2, wherein the processing the site-centric data and the user-centric data to form the calibration value further includes:

processing, by the server, the user-centric data in relation to the measured interactions of the users in the sample group of monitored users.

4. The computer-implemented method according to claim 1, wherein the combining the site-centric data and the user-centric data is accomplished via a reporting server connected to the communications network and includes at least one of:

displaying, via the reporting server, the single data source,

aggregating, via the reporting server, the single data source,

transforming, via the reporting server, the single data source,

calibrating, via the reporting server, the single data source, and

formatting, via the reporting server, said single data source.

5. The computer-implemented method according to claim 1, further comprising:

processing, by the server, the site-centric data and the user-centric data.

6. The computer-implemented method according to claim 1, wherein the obtaining the site-centric data includes:

obtaining, via the server, measurements of interactions of all users of the first group of one or more monitored resources using a measurement code unit corresponding to the first group.

7. The computer-implemented method according to claim 1, wherein the obtaining the user-centric data includes:

obtaining, by the server, a measurement code unit from the user interface of the users in the sample group; and

recording, by the server, interactions of each user in the sample group based on the measurement code unit.

8. The computer-implemented method according to claim 1, further including:

multiplying the weighting factor with a number of users in the sample group that have corresponding interactions recorded in relation to the one or more unmonitored resources to obtain a second figure for an expected number of all users to have recorded interactions with the one or more unmonitored resources.

9. The computer-implemented method according to claim 8, wherein the error rate is calculated by dividing a number of actual interactions in the site-centric data, pertaining to the one or more monitored resources in the first group, by the first figure.

10. The computer-implemented method according to claim 9, wherein the error rate is multiplied by the second figure to obtain an expected number of total users to have interactions in relation to the one or more unmonitored resources.

11. A non-transitory computer-readable medium having embedded thereon instructions executable by a processor, the instructions operable to cause a computer to execute a method, the method comprising:

obtaining site-centric data, wherein the site-centric data corresponds to a first group of one or more monitored resources;

obtaining user-centric data, wherein the user-centric data corresponds to a second group of one or more monitored resources;

combining the site-centric data and the user-centric data to form a single data source corresponding to usage information of one or more resources;

processing the site-centric data and the user-centric data to form a calibration value; and

calibrating, by the server, an unmonitored resource value corresponding to one or more unmonitored resources based on the calibration value by multiplying the unmonitored resource value by the calibration value;

wherein the processing to form a calibration value comprises;

calculating a weighting factor based on a number of users that have interactions recorded in relation to the second group of one or more monitored resources and a total number of users expected to have access to the one or more resources available through the communications network; and

multiplying the weighting factor with a number of users in the sample group that have interactions recorded in relation to the first group of one or more monitored resources to obtain a first figure for an expected number of all users to have interactions with the first group.

12. The computer-implemented method according to claim 11, further comprising:

forming, by the server, when the user-centric data is obtained with respect to a group of monitored users, a sample group of monitored users;

recording, by the server, interactions of users in the sample group of monitored users; and

measuring, by the server, the recorded interactions of the users in the sample group of monitored users.

13. The computer-implemented method according to claim 11, wherein the processing the site-centric data and the user-centric data to form the calibration value further includes:

processing, by the server, the user-centric data in relation to the measured interactions of the users in the sample group of monitored users.

14. The computer-implemented method according to claim 11, further comprising:

processing, by the server, the site-centric data and the user-centric data.

15. The computer-implemented method according to claim 11, wherein the obtaining the site-centric data includes:

obtaining, via the server, measurements of interactions of all users of the first group of one or more monitored resources using a measurement code unit corresponding to the first group.

16. The computer-implemented method according to claim 11, wherein the obtaining the user-centric data includes:

obtaining, by the server, a measurement code unit from the user interface of the users in the sample group; and

recording, by the server, interactions of each user in the sample group based on the measurement code unit.

17. The computer-implemented method according to claim 11, further including:

multiplying the weighting factor with a number of users in the sample group that have corresponding interactions recorded in relation to the one or more unmonitored resources to obtain a second figure for an expected number of all users to have recorded interactions with the one or more unmonitored resources.

18. The computer-implemented method according to claim 17, wherein the error rate is calculated by dividing a number of actual interactions in the site-centric data, pertaining to the one or more monitored resources in the first group, by the first figure.