A method and apparatus for constructing a computational flow graph for time series processing

Abstract

The invention provides a method and an apparatus for constructing a computational flow graph of time series processing, wherein, the method comprises the following steps of: acquiring a time series processing algorithm to be processed; Splitting the time series processing algorithm to be processed into a plurality of calculation expressions; Constructing a computational flow graph for the plurality of computational expressions to obtain a plurality of computational flow graphs; The plurality of computational flow diagrams are combined to obtain a flow diagram model corresponding to the time series processing algorithm to be processed. As a result of that technical solution provide by the embodiments of the present application, the data dependency of time series algorithm can be visually displayed, this dependency can be used to optimize the computational steps in the time series processing algorithm, The problem of processor congestion caused by data dependency can be avoided, and thecomputational flow diagram can be easily divided according to the characteristics of the computing system to match the computing power of the computing system, so as to improve the resource utilization rate of the computing system and the running efficiency of the time series processing algorithm.

Description

technical field [0001] The present application belongs to the technical field of data processing, and in particular relates to a calculation flow graph construction method and device for time series processing. Background technique [0002] Time series analysis is an important technology in many analysis scenarios. In many application scenarios, it is generally necessary to be able to quickly respond to data streams based on fast and continuous arrivals. Taking the stock market application as an example, there are multiple clients monitoring the price fluctuations of the stock market, and the system needs to be able to effectively respond to analysis requests for different clients. The analysis requests of different clients are different, for example, some clients request the weekly stock price volatility, some clients request the monthly average stock price, etc. In addition to the financial field, time series analysis is also widely required in the fields of medical healt...

AI Technical Summary

Problems solved by technology

However, there are many types of time series processing functions, which makes the relationship between time series data very complicated

[0004] For the above problems, no effective solution has been proposed

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