Intelligent sampling of data generated from usage of interactive digital properties

Patent metadata

Field	Value
Publication number	US-2020296177-A1
Application number	US-201916354674-A
Country	US
Kind code	A1
Filing date	Mar 15, 2019
Priority date	Mar 15, 2019
Publication date	Sep 17, 2020
Grant date	—

Abstract

Techniques for tailoring sampling rates for data from interactive digital properties on a feature-by-feature basis and collecting the data using the tailored sampling rates. Each feature may have an independent sampling rate irrespective of sampling rates assigned to other features. The independent sampling rates are determined based on at least one factor of predictive feature usage information based on historical feature usage information, predetermined rules, and current usage velocity of the feature. In some embodiments the independent sampling rate is influenced by the usage of an allocated resource provided to the digital property relative to a total allocation of that resource for a given time period. In some embodiments, the allocated resource is server calls to a digital data analytics server for the purposes of providing feature usage information from the interactive digital property for the performance of digital data analytics.

First claim

1 . A computer-implemented method (CIM) comprising:
determining a predictive usage data set including information indicative of anticipated usage of a set of features of a digital property; determining a subset of features of the digital property for application of a data sampling threshold; for each feature of the subset of features, determining a sampling threshold value based, at least in part, on the predictive usage data set; and responsive to usage of the subset of features by a plurality of users over a computer network, generating a user data collection data set based, at least in part, on the determined sampling threshold value(s).

2 . The CIM of claim 1 , wherein generating the user data collection data set further comprises:
for each user of a digital property, assigning a sample inclusion value; and for each given feature of the subset of features, determining a subset of user interactions with the feature for inclusion in data collection of user-feature interactions based, at least in part, on the sampling threshold value of a given feature and the sample inclusion value of each user.

3 . The CIM of claim 1 , wherein the sampling thresholds are based, at least in part, on a proportion of total consumption of digital data analytics server calls by the digital property compared to the total digital data analytics server call allotment of the digital property.

4 . The CIM of claim 1 , wherein the sampling threshold value is based, at least in part, on velocity of usage of the feature.

5 . The CIM of claim 1 , wherein the predictive usage data set is based, at least in part, on historical usage data including information indicative of historical usage of at least some features of the digital property.

6 . The CIM of claim 1 , further comprising:
aggregating data including information indicative of usage of the set of features of the digital property, including: (i) data that has been sampled and multiplied, and (ii) data that has not been subject to a sampling threshold; wherein: data resulting from usage of at least some features of the digital property are not subject to a sampling threshold; data gathered from each given feature of the subset of features is multiplied by a term correlated to the sample threshold value to approximate unsampled usage of each given feature.

7 . A computer program product (CPP) comprising:
a machine readable storage device; and computer code stored on the machine readable storage device, with the computer code including instructions for causing a processor(s) set to perform operations including the following:
determining a predictive usage data set including information indicative of anticipated usage of a set of features of a digital property;
determining a subset of features of the digital property for application of a data sampling threshold;
for each feature of the subset of features, determining a sampling threshold value based, at least in part, on the predictive usage data set; and
responsive to usage of the subset of features by a plurality of users over a computer network, generating a user data collection data set based, at least in part, on the determined sampling threshold value(s).

8 . The CPP of claim 7 , wherein generating the user data collection data set further comprises:
for each user of a digital property, assigning a sample inclusion value; and for each given feature of the subset of features, determining a subset of user interactions with the feature for inclusion in data collection of user-feature interactions based, at least in part, on the sampling threshold value of a given feature and the sample inclusion value of each user.

9 . The CPP of claim 7 , wherein the sampling thresholds are based, at least in part, on a proportion of total consumption of digital data analytics server calls by the digital property compared to the total digital data analytics server call allotment of the digital property.

10 . The CPP of claim 7 , wherein the sampling threshold value is based, at least in part, on velocity of usage of the feature.

11 . The CPP of claim 7 , wherein the predictive usage data set is based, at least in part, on historical usage data including information indicative of historical usage of at least some features of the digital property.

12 . The CPP of claim 7 , wherein the computer code further includes instructions for causing the processor(s) set to perform the following operations:
aggregating data including information indicative of usage of the set of features of the digital property, including: (i) data that has been sampled and multiplied, and (ii) data that has not been subject to a sampling threshold; wherein: data resulting from usage of at least some features of the digital property are not subject to a sampling threshold; data gathered from each given feature of the subset of features is multiplied by a term correlated to the sample threshold value to approximate unsampled usage of each given feature.

13 . A computer system (CS) comprising:
a processor(s) set; a machine readable storage device; and computer code stored on the machine readable storage device, with the computer code including instructions for causing the processor(s) set to perform operations including the following:
determining a predictive usage data set including information indicative of anticipated usage of a set of features of a digital property;
determining a subset of features of the digital property for application of a data sampling threshold;
for each feature of the subset of features, determining a sampling threshold value based, at least in part, on the predictive usage data set; and
responsive to usage of the subset of features by a plurality of users over a computer network, generating a user data collection data set based, at least in part, on the determined sampling threshold value(s).

14 . The CS of claim 13 , wherein generating the user data collection data set further comprises:
for each user of a digital property, assigning a sample inclusion value; and for each given feature of the subset of features, determining a subset of user interactions with the feature for inclusion in data collection of user-feature interactions based, at least in part, on the sampling threshold value of a given feature and the sample inclusion value of each user.

15 . The CS of claim 13 , wherein the sampling thresholds are based, at least in part, on a proportion of total consumption of digital data analytics server calls by the digital property compared to the total digital data analytics server call allotment of the digital property.

16 . The CS of claim 13 , wherein the sampling threshold value is based, at least in part, on velocity of usage of the feature.

17 . The CS of claim 13 , wherein the predictive usage data set is based, at least in part, on historical usage data including information indicative of historical usage of at least some features of the digital property.

18 . The CS of claim 13 , wherein the computer code further includes instructions for causing the processor(s) set to perform the following operations:
aggregating data including information indicative of usage of the set of features of the digital property, including: (i) data that has been sampled and multiplied, and (ii) data that has not been subject to a sampling threshold; wherein: data resulting from usage of at least some features of the digital property are not subject to a sampling threshold; data gathered from each given feature of the subset of features is m

Assignees

• IBM

Inventors

• Hammer Stephen C
• Cannon Gray
• Baughman Aaron K

Classifications

• H04L67/535 Primary

  Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34) · CPC title

• H04L67/146

  Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06F16/43

  Querying · CPC title

• H04L67/10

  in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) · CPC title