Approximating activity loads in databases using smoothed time series
AssigneeInternational Business Machines Corporation
InventorsOfer Haim Biller, Oded Sofer
Life of this patent
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Abstract
An example system includes a processor to monitor activity on a database server to generate an events stream. The processor can convert the events stream into a time series that approximates activity load at the database server using an exponential smoothing. The processor can also send the time series to a streaming analytics engine.
Claims20
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.
A computer-implemented method, comprising:
- monitoring, via a processor, activity on database server to generate an events stream;
- converting, via the processor, the events stream into a time series;
- approximating, 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;
- sending, via the processor, the time series to a streaming analytics engine;
- and
- generating a trained machine learning model for the stream analytics engine for analyzing the time series using the plurality of simulated time windows in parallel.
A computer program product for approximating activity loads in databases, the computer program product comprising a computer-readable storage medium having program code embodied therewith, the program code executable by a processor to cause the 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.
Dependencies are inferred from the claim text.