Swinging Door Trending (SDT) Filter Processor
Description
The Swinging Door Trending (SDT) algorithm is a linear trend compression algorithm.
In essence, it replaces a series of continuous (timestamp, value) points with a straight line determined by the start and end points.
The Swinging Door Trending (SDT) Filter Processor can extract and forward the characteristic events of the original stream. In general, this filter can also be used to reduce the frequency of original data in a lossy way.
Required Inputs
The processor works with any input event that has one field containing a timestamp and one field containing a numerical value.
Configuration
Timestamp Field
Specifies the timestamp field name where the SDT algorithm should be applied on.
Value Field
Specifies the value field name where the SDT algorithm should be applied on.
Compression Deviation
Compression Deviation is the most important parameter in SDT that represents the maximum difference between the current sample and the current linear trend.
Compression Deviation needs to be greater than 0 to perform compression.
Compression Minimum Time Interval
Compression Minimum Time Interval is a parameter measures the time distance between two stored data points, which is used for noisy reduction.
If the time interval between the current point and the last stored point is less than or equal to its value, current point will NOT be stored regardless of compression deviation.
The default value is 0 with time unit ms.
Compression Maximum Time Interval
Compression Maximum Time Interval is a parameter measure the time distance between two stored data points.
If the time interval between the current point and the last stored point is greater than or equal to its value, current point will be stored regardless of compression deviation.
The default value is 9,223,372,036,854,775,807(Long.MAX_VALUE) with time unit ms.
Output
The characteristic event stream forwarded by the SDT filter.