US 12,001,310 B2
Approximating activity loads in databases using smoothed time series
Ofer Haim Biller, Neve Boker (IL); and Oded Sofer, Midreshet Ben Gurion (IL)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Apr. 5, 2021, as Appl. No. 17/222,010.
Prior Publication US 2022/0318119 A1, Oct. 6, 2022
Int. Cl. G06F 11/34 (2006.01); G06F 11/30 (2006.01); G06F 16/22 (2019.01)
CPC G06F 11/3419 (2013.01) [G06F 11/302 (2013.01); G06F 16/22 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising a processor to:
monitor activity on a database server to generate an events stream;
convert the events stream into a time series;
approximate, in response to detecting each event in the events stream, an activity load at the database server using an exponential smoothing, a current time stamp generated at the time of each detected event, and a previous time stamp generated at a previous event time, wherein a plurality of simulated time windows for analyzing the activity load are calculated using a smoothing factor of the exponential smoothing;
send the time series to a streaming analytics engine; and
generate a trained machine learning model for the stream analytics engine for analyzing the time series using the plurality of simulated time windows in parallel.