Systems and methods for event tracking using time-windowed counters
To allow for tracking events and classifying assets within a social networking system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset is classified based on comparison of the first signal value and the second signal value. In an embodiment, the time series is based on at least one time window including time intervals. In an embodiment, counters to determine a number of occurrences of an event type are associated with the time intervals. In an embodiment, each of the counters are incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.
1. A computer implemented method comprising:
generating, by a computer system, at least one time series of event occurrences of an event type within at least one time window having one or more time intervals, wherein the at least one time series is associated with at least one asset;
determining, by the computer system, a first signal value and a second signal value based on the at least one time series of the event occurrences; and
classifying, by the computer system, the at least one asset based on comparison of the first signal value and the second signal value;
resetting a counter associated with a least recent time interval of the time intervals; and
incrementing the counter upon an occurrence of the event type associated with the at least one asset during a most recent time interval.
2. The computer implemented method of claim 1 , wherein at least one of the first signal value and the second signal value is based on uniqueness.
3. The computer implemented method of claim 1 , wherein generating the at least one time series includes associating counters respectively with the time intervals, wherein each of the counters are for tracking the number of event occurrences of the event type.
4. The computer implemented method of claim 3 , further comprising incrementing each of the counters upon an occurrence of the event type associated with the at least one asset during an associated time interval.
5. The computer implemented method of claim 1 , wherein the at least one time window is implemented as a circular buffer.
6. The computer implemented method of claim 5 , wherein the circular buffer comprises elements representing the time intervals.
7. The computer implemented method of claim 1 , wherein the at least one time window is implemented as a linear buffer.
8. The computer implemented method of claim 7 , wherein the linear buffer is associated with at least one of exponentially-decaying time intervals and non-decreasing time intervals.
9. The computer implemented method of claim 7 , wherein the linear buffer comprises elements representing the time intervals.
10. The computer implemented method of claim 1 , further comprising:
providing a value from a first counter associated with a first time interval to a second counter associated with a second time interval;
resetting the first counter; and
incrementing the first counter upon an occurrence of the event type associated with the at least one asset.
11. The computer implemented method of claim 1 , wherein the classifying comprises:
generating a score based on a procedure incorporating the first signal value and the second signal value; and
assigning the at least one asset to a classification based on the score.
12. The computer implemented method of claim 11 , wherein the procedure comprises at least one of a decision tree and a machine learning model.
13. The computer implemented method of claim 11 , wherein the classification comprises at least one of a spam category, a popularity category, and a ranking.
14. The computer implemented method of claim 1 , wherein the determining comprises calculating an integral of at least one function.
15. The computer implemented method of claim 1 , further comprising modifying a policy based on classification of the at least one asset.
16. The computer implemented method of claim 1 , wherein at least one of the generating, the determining, and the classifying is performed in real time.
17. A system comprising:
at least one processor; and
a memory storing instructions configured to instruct the at least one processor to perform:
generating at least one time series of event occurrences of an event type within at least one time window having one or more time intervals, wherein the at least one time series is associated with at least one asset;
determining a first signal value and a second signal value based on the one or more time intervals in the at least one time series;
classifying the at least one asset based on comparison of the first signal value and the second signal value; and
wherein the at least one time series is associated with at least one of exponentially-decaying time intervals and non-decreasing time intervals.
18. A non-transitory computer storage medium storing computer-executable instructions that, when executed, cause a computer system to perform a computer-implemented method comprising:
generating at least one time series of event occurrences of an event type within at least one time window having one or more time intervals, wherein the at least one time series is associated with at least one asset;
determining a first signal value and a second signal value based on the at least one time series;
classifying the at least one asset based on comparison of the first signal value and the second signal value; and
providing a value from a first counter associated with a first time interval to a second counter associated with a second time interval;
resetting the first counter; and
incrementing the first counter upon an occurrence of the event type associated with the at least one asset.