Time series data processing method, device and electronic device

By aggregating and encoding the original timing data of user behavior, encoding timing data is generated, and the problems of high resource occupancy of timing data storage and low query efficiency are solved, and the data volume is reduced and processing efficiency is improved.

CN114064753BActive Publication Date: 2025-03-25ALIBABA (CHINA) CO LTD
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Patent Information

Application Number
CN202111330833.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2021-11-11
Publication Date
2025-03-25
Estimated Expiration
2041-11-11

AI Technical Summary

Technical Problem

In the prior art, the storage resource occupancy rate of time-series data and the data query efficiency are too low, which are mainly due to the database storage and query efficiency problems caused by the large amount of time-series data.

Method used

By aggregating and encoding the original timing data of user behavior, encoded timing data is generated, reducing the amount of data and improving storage and query efficiency. The specific steps include: aggregating the time series data according to the time of behavior occurrence, encoding the user identity, behavior occurrence time and behavior indicators, generating an identification array, time array and index array, and finally generating the coded time series data for storage.

Benefits of technology

Without losing data content, the amount of data is greatly reduced, the processing efficiency of data storage and query is improved, and the problems of high resource occupancy and low query efficiency are effectively solved.

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Abstract

The time series data processing method, device and electronic device provided in the embodiments of the present invention aggregate and encode the original time series data of the obtained user behavior, so that the data content of multiple original time series data can be represented by one encoded time series data, thereby greatly reducing the data volume without losing the data content and improving the processing efficiency of data storage and query.
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