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Big data task draggable modeling method and system, storage medium and terminal

A modeling method and big data technology, applied in the field of data modeling, can solve the problems of lack of professionalism and rigor, and achieve the effect of easy to use, simple operation and simple tasks

Active Publication Date: 2021-12-21
ZHEJIANG GEESPACE TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Open-source drag-and-drop machine learning mining modeling only supports simple linear regression and logistic regression algorithms, while some popular foreign algorithms and integrated algorithms are not connected at present, lacking professionalism and rigor to keep pace with the times

Method used

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  • Big data task draggable modeling method and system, storage medium and terminal
  • Big data task draggable modeling method and system, storage medium and terminal

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Embodiment Construction

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0041] In the several embodiments provided in this application, the described system embodiments are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as Multiple modules or functional components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In another point, the mutual cou...

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Abstract

The invention relates to the field of data modeling, and provides a big data task draggable modeling method and system, a storage medium and terminal device. The method comprises the steps of determining the task type of a target task, and uploading source data of the target task; determining a processing flow of the target task based on dragging tracks of the functional components and a line connection sequence among the functional components, and determining an analyzer of the target task according to the task type; and triggering the analyzer to analyze a processing flow of the target task, and processing the source data according to the processing flow to obtain a processing result of the target task. The modeling process is realized by dragging and connecting the components, the big data modeling task is simplified, and the invention is simple to operate and easy to master.

Description

technical field [0001] The invention relates to the technical field of data modeling, in particular to a drag-and-drop modeling method, system, storage medium and terminal for big data tasks. Background technique [0002] The drag-and-drop technology is developed from the wizard-style technology. It can realize the complex combination of multifunctional components and easy use. The existing drag-and-drop modeling includes a drag-and-drop big data ETL tool SDC (Streamsets DataCollector) tool and Ali PAI (Platform of Artificial Intelligence), etc., SDC is a drag-and-drop big data ETL tool, which only realizes the design of draggable functional components for ETL and other processing processes. Ali PAI is mainly for enterprises and developers. The native machine learning platform is complex to use and cannot be used directly by beginners. However, the task modeling and data mining functions of big data need to be flexible and easy to use for beginners, while traditional technol...

Claims

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Application Information

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IPC IPC(8): G06F8/34G06F3/0486G06F16/21G06F16/27G06N20/00
CPCG06F8/34G06F3/0486G06F16/212G06F16/27G06N20/00Y02D10/00
Inventor 刘大忠
Owner ZHEJIANG GEESPACE TECH CO LTD
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