US 11,669,382 B2
Anomaly detection for data stream processing
Jacob Barton Leverich, San Francisco, CA (US); Shang Cai, Burnaby (CA); Hongyang Zhang, Vancouver (CA); Mihai Ganea, Vancouver (CA); and Alex Cruise, Vancouver (CA)
Assigned to Splunk Inc., San Francisco, CA (US)
Filed by SPLUNK INC., San Francisco, CA (US)
Filed on Dec. 20, 2019, as Appl. No. 16/722,673.
Application 16/722,673 is a continuation of application No. 16/176,186, filed on Oct. 31, 2018, granted, now 10,558,516.
Application 16/176,186 is a continuation of application No. 15/206,126, filed on Jul. 8, 2016, granted, now 10,146,609, issued on Dec. 4, 2018.
Prior Publication US 2020/0125433 A1, Apr. 23, 2020
Int. Cl. G06F 11/07 (2006.01)
CPC G06F 11/079 (2013.01) [G06F 11/0709 (2013.01); G06F 11/0793 (2013.01)] 30 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
accessing an anomaly detection definition that defines how to populate a buffered signal of a sequential set of time-series data points;
determining that one of a plurality of data streams is associated with the anomaly detection definition; and
upon receiving an updated data point from the data stream, performing anomaly detection by:
inserting the updated data point from the data stream into the buffered signal of the sequential set of time-series data points; and
analyzing the buffered signal of the sequential set of time-series data points to determine a corresponding anomaly result for the anomaly detection definition.