A processing method for querying time-series data increment times and decrement times

Abstract

The invention discloses a processing method for querying time-series data increment times and decrement times. The invention groups the data sequence in advance, and then calculates the cumulative increment and decrement times, the first element and the tail element of each grouping, and stores the intermediate results in the corresponding On the timestamp, the calculation result can be quickly obtained according to the specified timestamp during query; first, the storage location of the intermediate result is flexible and changeable to meet the needs of various scenarios; since the storage result is the intermediate result after calculation, the memory consumption is reduced and the storage efficiency is improved Very high, because the intermediate results have been calculated in advance, so the response speed is extremely fast when obtaining the corresponding increment times and decrement times; the most important thing is that this part of the results changes continuously with the system time movement, and can achieve the required increment The purpose of the number of times and the number of decrements moves smoothly with the time window. The invention is applicable to data analysis fields such as time series data processing technology, and can significantly increase the speed of counting increasing times and decreasing times.

Description

technical field [0001] The invention is a time-series data processing technology. According to the time stamp in the time-series data, the time-series calculation result of a specified interval is stored on the corresponding time stamp of the interval, thereby providing the capability of time window parallel sliding processing. It is mainly applicable to data analysis fields such as time series data processing technology. Background technique [0002] In the field of time series data analysis and processing, there are many solutions for time window sliding processing, most of which filter the data on an entity to retain the original data, so as to perform adhoc query. Adhoc query is very flexible, but once the entity is too large (data is too dense), the amount of data that needs to be processed each time increases, and the performance will be very low. [0003] Suppose there is an account with the following sequence, ⑦ ⑤⑥ ② Arranged as figure 1 shown. Assuming th...

AI Technical Summary

Problems solved by technology

Because the calculation needs to be repeated based on the original time series every time, the whole calculation process is complex and inefficient. The only advantage is that the calculation is more flexible.

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