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Consistency detection model construction method based on ParallModCTANE

A construction method and detection model technology, applied in the direction of structured data retrieval, digital data information retrieval, instruments, etc., can solve the problems of reducing the consistency detection accuracy of water regime business data, so as to improve efficiency, improve efficiency, and improve accuracy rate effect

Pending Publication Date: 2022-05-10
HOHAI UNIV
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Problems solved by technology

Under a single data node, the results of conditional function dependency mining may be limited, and only work on a single data node. After data exchange, these conditional function dependencies may not be meaningful to other nodes, which will greatly Reduce the accuracy of water regime business data consistency detection

Method used

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  • Consistency detection model construction method based on ParallModCTANE
  • Consistency detection model construction method based on ParallModCTANE
  • Consistency detection model construction method based on ParallModCTANE

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] Embodiment 1: In this embodiment, consistency detection is performed on the water regime business data of the Yangtze River Committee, such as figure 1 shown, including the following steps.

[0051] The data set of this embodiment is based on the water regime business data of the Yangtze River Committee, and uses the Yangtze River Committee's Xinjiangkou, Wayao River (2), Wayao River, Ouchi (Kangsan), and Ouchi (Tube) 5 measuring stations The ten-month average water level and flow data of the node is used for parallel mining. Among them, there are 2,784 pieces of Xinjiangkou station data, 2,783 pieces of Wayaohe (II) station data, 2,784 pieces of Wayao and station data, 2,692 pieces of Ouchi (Kangsan) station, and 2,784 pieces of Ouchi (Tube) station. The table name is HIA_DCMZQ_S. After preliminary cleaning, there are a total of 20 attributes and a total of 13827 pieces of data.

[0052]In the experiment in step S1, the number of distributed server nodes is set to 2,...

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Abstract

The invention discloses a consistency detection model construction method based on ParallModCTANE. The method comprises the following steps: improving a CTANE algorithm; carrying out distributed parallel conditional function dependency mining on the hydrological data; filtering the conditional function dependency set; and performing linked table inconsistency detection on the hydrological data based on the main data to obtain an inconsistency detection result of the hydrological data. According to the method, after the data is subjected to preliminary cleaning, distributed parallel conditional function dependency mining is performed in combination with a ParallModCTANE method, so that the efficiency of conditional function dependency mining is higher, and the detection efficiency is higher by using a linked table inconsistency detection algorithm based on the main data to perform consistency detection.

Description

technical field [0001] The invention belongs to the technical field of data quality control, and in particular relates to a method for constructing a consistency detection model based on Parallel_ModCTANE. Background technique [0002] Big data is a large-scale data collection, which far surpasses traditional software in storage and management analysis, so that it is impossible to use existing database management systems for data storage, search, analysis, etc., but must pass through dozens of, Hundreds or even larger server clusters for parallel processing. The core value of big data lies in the storage and analysis of massive data; therefore, the strategic significance of big data-related technologies lies not in mastering a large amount of data information, but in professionally processing meaningful data. [0003] In the context of distributed hydrological big data, if there is inconsistency in the data, how to find out the conditional function dependence implicit in th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06F16/27G06F16/21G06F16/242G06F16/22
CPCG06F16/2465G06F16/27G06F16/212G06F16/2433G06F16/2282
Inventor 王潇凯万定生余宇峰
Owner HOHAI UNIV
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