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A kind of machine learning method, device and big data platform

A machine learning and database technology, applied in the field of big data, can solve problems such as modeling, prediction and application reliability reduction, programming flexibility, easy-to-maintain code or component reusability, and reduce system performance, etc., to achieve adaptation generality and versatility, improving the efficiency of development and deployment, and the effect of improving deployment efficiency

Active Publication Date: 2019-03-01
华云工业互联网有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the technical solution of the machine learning algorithm model based on spark big data in the existing technology is very complicated, and the technical level of the design covers distributed computing, architecture deployment, model computing, data development and other aspects, and it takes a lot of manpower and material resources to complete
Each process requires frequent mutual access operations with the file system, which greatly reduces the performance of the entire system, resulting in reduced reliability of modeling, prediction, and applications; more importantly, it will lead to programming flexibility, ease of maintenance, and The reusability of code or components is greatly affected, resulting in poor user experience

Method used

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  • A kind of machine learning method, device and big data platform
  • A kind of machine learning method, device and big data platform
  • A kind of machine learning method, device and big data platform

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0043] Please refer to figure 1 and figure 2 A specific implementation of a machine learning apparatus of the present invention is shown.

[0044] In this embodiment, a machine learning device includes: a user-defined process module 1 , a configuration module 4 , a database 3 , and an event server 2 . The user-defined flow module 1 includes a logic that can receive the executable file contained in the request initiated by the user, and is called by the event server 2 . The database 3 binds the front-end development application to the executable file through the configuration file written by the configuration module 4 . Specifically, the executable file includes an executable program, a computer component, a system plug-in, a visual interface application or a computer-executable document.

[0045] The user-defined flow module 1 includes an interface module 11 , a business logic module 12 , a service module 13 and a performance evaluation module 14 . Specifically, see fig...

Embodiment 2

[0057] combined reference image 3 As shown, the main difference between this embodiment and the first embodiment is that in this embodiment, the machine learning device further includes an encryption module 5, which binds the RESTfull API by accessing keywords, so as to associate the configuration file with the Execute the file to bind. Preferably, the access key is an Access key or a Secret key. The front-end application interacts with the model time server 6 by binding the Access Key to the RESTful API service to complete the data query service.

[0058] Please refer to the description of Embodiment 1 for the same technical solution in this embodiment as in Embodiment 1, which will not be repeated here.

Embodiment 3

[0060] ginseng Figure 4 As shown, this embodiment discloses a machine learning method, which includes the following steps:

[0061] S1. The executable file contained in the request initiated by the user is received by the user-defined process module;

[0062] S2, call the executable file to the event server;

[0063] S3. Build a configuration file according to the user's environment variables;

[0064] S4. Bind the front-end development application to the executable file according to the content of the configuration file in the database.

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Abstract

The invention discloses a machine learning device, a machine learning method based on the machine learning device, and a big data platform using the machine learning device and the machine learning method thereof. The machine learning device comprises a user-defined process module, a configuration module, a database and an event server, wherein the user-defined process module comprises a logic module, the logic module can receive an executable file contained in a request initiated by a user, and can be called by the event server; and the database binds a front-end development application with the executable file through a configuration file written by the configuration module. According to the machine learning method, the machine learning device and the big data platform, the service logic component is completed by means of the user-defined process module, the adaptability and universality of the machine learning method, the machine learning device and the big data platform for various application scenarios are realized, efficient operation of data mining and machine learning involved in the development process of standardized big data are achieved, the development process of the standardized big data is simplified, and the development and deployment efficiency of the standardized big data is improved.

Description

technical field [0001] The invention relates to the technical field of big data, in particular to a machine learning method, a machine learning device, and a big data platform based on the machine learning device. Background technique [0002] Spark is an open source big data computing and processing engine of Databricks. It became a top-level project of Apache in 2010. Its core computing is Resilient Distributed Data Set (RDD), which provides a MapReduce model that is richer than Hadoop and can quickly perform in-memory processing of data sets. Iterative computing supports complex machine learning algorithms and graph theory algorithms. [0003] The machine learning methods in the prior art are as follows. [0004] First, perform step 1) collection of raw data: the data producer will generate various types of data, such as log files, image data, text data, etc., and the data quality will occur with the inappropriate behavior of users or some problems in the system A lot o...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 许广彬郑军张银滨强亮周曙刚段石石
Owner 华云工业互联网有限公司
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